2024-03-29T07:35:45Z
https://data.csiro.au/oaiprovider/
csiro:9634
2020-12-18T05:38:09Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/14041
https://data.csiro.au/dap/
102.100.100/14041
10.4225/08/577DC332AA649
Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 1" mosaic data
http://hdl.handle.net/102.100.100/14041?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/577DC332AA649
Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 1" mosaic data
v4
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_324
Monthly outgoing longwave radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050209
050302
050104
050399
050205
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format.
The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18335 .
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18336
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:18335
1 arcsecond resolution tiled data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 1" tiled data. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18336
3 arcsecond resolution mosaic data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 3" mosaic data. v3. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:9635
2020-12-18T06:48:56Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/14038
https://data.csiro.au/dap/
102.100.100/14038
10.4225/08/579965DB0FFD9
SRAD sky view factor modelled using the 1" DEM-S
http://hdl.handle.net/102.100.100/14038?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/579965DB0FFD9
SRAD sky view factor modelled using the 1" DEM-S
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_329
csiro:service_328
Sky view factor
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050104
050302
050205
050209
050399
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The data in this collection are available at 1 arcsecond resolution as 1x1 degree tiles (ESRI grid format) or as a single grid mosaic of Australia (TIFF format), and at 3 arcsecond resolution as a single grid of Australia (TIFF format).
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:9989
2023-06-16T06:18:41Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16109
https://data.csiro.au/dap/
102.100.100/16109
10.4225/08/546EE212B0048
Soil and Landscape Grid National Soil Attribute Maps - Bulk Density - Whole Earth (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16109
1950-01-01
2013-12-31
northlimit=-9.9983; southlimit=-43.6425; westlimit=112.9125; eastLimit=153.6400; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546EE212B0048
Soil and Landscape Grid National Soil Attribute Maps - Bulk Density - Whole Earth (3" resolution) - Release 1
v6
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2023-06-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_950
csiro:service_952
csiro:service_951
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Bulk Density
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
BD
SLGA
410699
This is Version 1 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method;
Units: g/cm3;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Variance explained (cross-validation): 0.4%;
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being;
1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a);
2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).
Version 1 of the Australian Soil Property Maps combines mapping from the:
1) Australia-wide three-dimensional Digital Soil Property Maps;
2) Western Australia Polygon Disaggregation Maps;
3) South Australian Agricultural Areas Polygon Disaggregation Maps;
4) Tasmanian State-wide DSM Maps.
These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18336
2020-12-18T05:53:20Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39412
https://data.csiro.au/dap/
102.100.100/39412
10.4225/08/577B02E8CF646
Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 3" mosaic data
http://hdl.handle.net/102.100.100/39412?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/577B02E8CF646
Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 3" mosaic data
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_326
Monthly outgoing longwave radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050209
050302
050104
050205
050399
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format.
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18335 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9634
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9634
1 arcsecond resolution mosaic data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 1" mosaic data. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18335
1 arcsecond resolution tiled data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly outgoing surface longwave radiation modelled using the 1" DEM-S - 1" tiled data. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18671
2020-12-18T05:33:02Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39954
https://data.csiro.au/dap/
102.100.100/39954
10.4225/08/579802C242E01
Mean monthly net radiation modelled using the 1" DEM-S - 3" mosaic
http://hdl.handle.net/102.100.100/39954?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/579802C242E01
Mean monthly net radiation modelled using the 1" DEM-S - 3" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_323
Monthly net radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050302
050205
050399
050209
050104
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format.
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces by aggregating the cells in a 3x3 window and taking the mean value.
The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9630 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18670
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9630
1 arcsecond resolution tiled data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net radiation modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18670
1 arcsecond resolution mosaic data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net radiation modelled using the 1" DEM-S - 1" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18731
2021-01-12T00:35:51Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39962
https://data.csiro.au/dap/
102.100.100/39962
10.4225/08/5799D4A672AFF
Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 1" mosaic
http://hdl.handle.net/102.100.100/39962?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/5799D4A672AFF
Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 1" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2021-01-12
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_332
Monthly shortwave radiation ratio
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050205
050302
050399
050209
050104
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9631 .
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18732
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9631
1 arcsecond resolution tiles
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18732
3 arcsecond resolution mosaics
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 3" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18732
2020-12-18T05:53:20Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39959
https://data.csiro.au/dap/
102.100.100/39959
10.4225/08/5799819AE0D1C
Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 3" mosaic
http://hdl.handle.net/102.100.100/39959?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/5799819AE0D1C
Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 3" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_325
Monthly shortwave radiation ratio
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050209
050104
050399
050205
050302
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format.
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9631 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18731
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9631
1 arcsecond resolution tiles
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18731
1 arcsecond resolution mosaics
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly shortwave radiation ratio modelled using the 1" DEM-S - 1" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:19311
2019-11-11T04:44:32Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/42094
https://data.csiro.au/dap/
102.100.100/42094
Fractional cover - MODIS, CSIRO algorithm
http://hdl.handle.net/102.100.100/42094?index=1
2001-01-01
2019-11-11
northlimit=70.0; southlimit=-60.0; westlimit=-180.0; eastLimit=180.0; projection=WGS84
csiro:42018
Fractional cover - MODIS, CSIRO algorithm
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Guerschman
Juan
2019-11-11
csiro:3d354ff0-9429-4460-8b09-4b236f327b86
csiro:R-12965_IRP
csiro:PartyGroup
Vegetation cover, vegetation area fraction, MODIS, fractional cover, ground cover, total cover
050104
050302
070101
050206
050199
050202
050209
Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution.
A suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series.
Monthly: The monthly product is aggregated from the 8-day composites using the medoid method.
Anomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%.
Decile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series.
MODIS fractional cover has been validated for Australia.
Version 3.0: Fractional cover was derived using a linear unmixing methodology (Guerschman et al. 2015). The method uses all 7 MODIS bands and adds log transforms and band interaction terms to account for non-linearities in the spectral mixing. A cross-validation step was also included to select the optimal number of singular values to avoid over-fitting. The calibration and validation steps used 1171 field observations across Australia. Overall, the model fitted and applied to MCD43A4 fractional cover has a root mean square error (RMSE) of 12.9%, 18.1% and 16.6% for the PV, NPV and BS fractions respectively (percentage cover)
Version 3.1: ("v310") Same as Version 3.0 with the following modifications: 1- input data changed to MODIS Collection 6.0 (MCD43A4.006) (see source). 2- calibration dataset expanded to include ~3022 field measurement sites accross Australia. 3- overall accuracy improved to an RMSE of 11.3%, 16.1% and 14.7% for the PV, NPV and BS fractions respectively.
Monthly vegetation cover is calculated from the 8-day composites using a medoid method as described in Gill et al.
Monthly anomalies show the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available\nMonthly deciles show the decile (ranking) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time series.
http://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/tc43/modis-fc/catalog.xml
NCI Thredds Server
Thredds Server at NCI
http://doi.org/10.1080/2150704X.2018.1465611
Calibration and validation of the Australian fractional cover product for MODIS collection 6
paper
http://dx.doi.org/10.1016/j.rse.2015.01.021
Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data
paper
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18491
2020-12-18T00:30:08Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39916
https://data.csiro.au/dap/
102.100.100/39916
10.4225/08/5788852154FC9
Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 1" mosaic
http://hdl.handle.net/102.100.100/39916?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/5788852154FC9
Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 1" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_318
Monthly incoming longwave radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050205
050104
050399
050302
050209
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9632 .
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18492
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9632
1 arcsecond resolution tiled data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18492
3 arcsecond resolution mosaic data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 3" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18492
2020-12-18T00:45:20Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39915
https://data.csiro.au/dap/
102.100.100/39915
10.4225/08/578884AA56FA2
Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 3" mosaic
http://hdl.handle.net/102.100.100/39915?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/578884AA56FA2
Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 3" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_319
Monthly incoming longwave radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050399
050209
050104
050205
050302
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format.
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9632 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18491
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:18491
1 arcsecond resolution mosaic data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 1" mosaic. v1. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:9632
1 arcsecond resolution tiled data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly incoming atmospheric longwave radiation modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18611
2020-12-18T03:57:09Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39922
https://data.csiro.au/dap/
102.100.100/39922
10.4225/08/57900F36C5113
Mean monthly net longwave radiation modelled using the 1" DEM-S - 1" mosaic
http://hdl.handle.net/102.100.100/39922?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/57900F36C5113
Mean monthly net longwave radiation modelled using the 1" DEM-S - 1" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_321
Monthly net longwave radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050104
050399
050209
050302
050205
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9633 .
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces by aggregating the cells in a 3x3 window and taking the mean value.
The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18612
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9633
1 arcsecond resolution tiles
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net longwave radiation modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18612
3 arcsecond resolution mosaic
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net longwave radiation modelled using the 1" DEM-S - 3" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18612
2020-12-18T01:20:42Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39918
https://data.csiro.au/dap/
102.100.100/39918
10.4225/08/578D8D40AC0A2
Mean monthly net longwave radiation modelled using the 1" DEM-S - 3" mosaic
http://hdl.handle.net/102.100.100/39918?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/578D8D40AC0A2
Mean monthly net longwave radiation modelled using the 1" DEM-S - 3" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_320
Monthly net longwave radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050399
050209
050104
050302
050205
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format.
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces by aggregating the cells in a 3x3 window and taking the mean value.
The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9633 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18611
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9633
1 arcsecond resolution tiled data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net longwave radiation modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18611
1 arcsecond resolution mosaic data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net longwave radiation modelled using the 1" DEM-S - 1" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18670
2020-12-18T05:33:02Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39955
https://data.csiro.au/dap/
102.100.100/39955
10.4225/08/57980D45BC556
Mean monthly net radiation modelled using the 1" DEM-S - 1" mosaic
http://hdl.handle.net/102.100.100/39955?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/57980D45BC556
Mean monthly net radiation modelled using the 1" DEM-S - 1" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2020-12-18
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_322
Monthly net radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050302
050104
050209
050399
050205
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9630 .
The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces by aggregating the cells in a 3x3 window and taking the mean value.
The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18671
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9630
1 arcsecond resolution tiled data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net radiation modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18671
3 arcsecond resolution mosaic data
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly net radiation modelled using the 1" DEM-S - 3" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:18851
2021-01-12T00:35:51Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/39968
https://data.csiro.au/dap/
102.100.100/39968
10.4225/08/57A922559C76A
Mean monthly total shortwave radiation on a sloping surface modelled using the 1" DEM-S - 1" mosaic
http://hdl.handle.net/102.100.100/39968?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/57A922559C76A
Mean monthly total shortwave radiation on a sloping surface modelled using the 1" DEM-S - 1" mosaic
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
Van Niel
Tom
2021-01-12
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
csiro:service_333
Monthly shortwave radiation
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050399
050209
050302
050104
050205
Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive:
• Incoming short-wave radiation on a sloping surface
• Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface)
• Incoming long-wave radiation
• Outgoing long-wave radiation
• Net long-wave radiation
• Net radiation
• Sky view factor
All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.
The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.
The monthly data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9530 .
The 3 arc-second resolution versions of these radiation surfaces have been produced from the 1 arc-second resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.
The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18852
Source data
1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)
2. Aspect derived from the 1 arcsecond SRTM DEM-S
3. Slope derived from the 1 arcsecond SRTM DEM-S
4. Monthly cloud cover fraction (Jovanovic et al., 2011)
5. Monthly albedo derived from AVHRR (Donohue et al., 2010)
6. Monthly minimum and maximum air temperature (Bureau of Meteorology)
7. Monthly vapour pressure (Bureau of Meteorology)
8. Monthly fractional cover (Donohue et al., 2010)
9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013)
10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)
Solar radiation model
Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for:
Annual variations in sun-earth distance
Solar geometry based on latitude and time of year
The orientation of the land surface relative to the sun
Shadowing by surrounding topography
Clear-sky and cloud transmittance
Sunshine fraction (cloud-free fraction of the day) in morning and afternoon
Surface albedo
The effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover
Atmospheric emissivity based on vapour pressure
All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.
Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.
References
Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.
Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.
Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.
Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.
NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.
https://data.csiro.au/dap/landingpage?pid=csiro:9530
1 arcsecond resolution tiles
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly total shortwave radiation on a sloping surface modelled using the 1" DEM-S - 1" tiles. v3. CSIRO. Data Collection.
https://data.csiro.au/dap/landingpage?pid=csiro:18852
3 arcsecond resolution mosaics
Gallant, John; Austin, Jenet; Van Niel, Tom (2014): Mean monthly total shortwave radiation on a sloping surface modelled using the 1" DEM-S - 3" mosaic. v1. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:20912
2019-12-05T14:09:10Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/44156
https://data.csiro.au/dap/
102.100.100/44156
10.25919/5de850a1d2172
3D Mineral mapping of Queensland - Version 2 ASTER and related geoscience products
http://hdl.handle.net/102.100.100/44156?index=1
2014-12-01
2016-12-01
northlimit=-10.0; southlimit=-30.0; westlimit=138.0; eastLimit=154.0; projection=WGS84
10.25919/5de850a1d2172
3D Mineral mapping of Queensland - Version 2 ASTER and related geoscience products
v4
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Cudahy
Tom
Jones
Mal
Lisitsin
Vladimir A.
Caccetta
Mike
Collings
Simon
Bateman
Roger
2019-12-05
csiro:10e65363-53b0-4d64-ab44-807fbb2e3ba7
csiro:R-06912_IRP
csiro:PartyGroup
Mineral mapping
3D
ASTER
HyMap
NVCL
NGSA
vegetation unmixiing
Geology
Alteration
Regolith
Queensland
Australia
Version 2
040499
040601
040306
040201
040399
050399
090905
059999
049999
The digital 3-dimenional (3D) mineral mapping suite of Queensland comprises ~20 “standardized” products at the spectral resolution of the ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) sensor and generated from publicly-available satellite, airborne, field and drill core spectral data spanning the visible near infrared (VNIR; 0.4 to 1.0 µm), shortwave infrared (SWIR; 1.0 to 2.5 µm) and thermal infrared (TIR; 7.5 to 12.0 µm) wavelength regions, including:
1. Satellite ASTER maps at both 30 m and 90 m pixel resolution with complete coverage of the state of Queensland, i.e. 1.853 million km²;
2. Airborne HyMap maps at ~5 m pixel resolution with a coverage of ~25,000 km2 from areas across north Queensland;
3. Field point samples (~300) from the National Geochemical Survey of Australia (NGSA) collected from a depth of 0-10 cm of flood overbank sediments;
4. Drill-core profiles (~20) of the National Virtual Core Library (NVCL) selected from the area around the Georgetown seismic line (07GA-IG2).
Key to the processing of the remote sensing data-sets (ASTER and HyMap) was the implementation of unmixing methods to remove the effects dry and green vegetation. This unmixing was not applied to the Australian ASTER geoscience maps released in 2012 (called here Version 1 or V1) resulting in extensive areas with little/no mineral information because of the need to apply masks. The vegetation unmixing methods used in the Version 2 (V2) processing of the ASTER and HyMap imagery has resulted in very few areas without coherent mineral information.
The resultant V2 “mineral group” products were designed to measure mineral information potentially useful for mapping: (i) primary rock composition; (ii) superimposed alteration effects; and (iii) regolith cover. These V2 products may assist in mapping soil properties and groundwater conditions. However their relatively low spectral resolution (based on ASTER’s 14 VNIR-SWIR-TIR bands) means that they do not provide the high level of mineralogical detail available from hyperspectral systems (>100 spectral bands), like HyMap and the HyLogger. Nevertheless, the relatively low spectral resolution of ASTER means that all other sensor data can be spectrally resampled to that resolution. Furthermore, the ASTER global data archive, which now spans entire Earth’s land surface <80degrees latitude, means that it can be used as global base-map for integrating all other spectral data.
The raw ASTER data used in this project are freely available from the United States Geological Survey (USGS) Land Processes Distributed Active Archive Centre (LPDAAC) (https://lpdaac.usgs.gov/dataset_discovery/aster/aster_products_table/ast_l1t) as well as NASA’s REVERB (http://reverb.echo.nasa.gov) and Japan’s Advanced Institute for Science Technology (AIST) https://gbank.gsj.jp/madas/map.
The Australian ASTER Geoscience (V1) Maps can be downloaded from CSIRO’s Data Access Portal (DAP) (https://data.csiro.au/dap/landingpage?pid=csiro%3A6182) and Geoscience Australia’s Australian Geoscience Information Network, Geoscience Australia (AUSGIN) (http://portal.geoscience.gov.au/gmap.html).
The NVCL data can be downloaded from http://www.auscope.org.au/nvcl or http://portal.geoscience.gov.au/gmap.html.
The National Geochemical Survey of Australia (NGSA) spectral data is accessible via CSIRO’s Data Access Portal http://www.ga.gov.au/about/projects/minerals-archive/concluded/national-geochemical-survey.
All Rights (including copyright) CSIRO, Geological Survey of Queensland 2017.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:5144
2020-08-25T04:48:41Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/8339
https://data.csiro.au/dap/
102.100.100/8339
10.4225/08/5758CCC862AD5
Topographic Position Index derived from 1" SRTM DEM-S
http://hdl.handle.net/102.100.100/8339?index=1
2000-02-11
2000-02-22
northlimit=-10.0; southlimit=-44.0; westlimit=113.0; eastLimit=154.0; uplimit=0.0; downlimit=0.0; projection=WGS84
10.4225/08/5758CCC862AD5
Topographic Position Index derived from 1" SRTM DEM-S
v6
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Gallant
John
Austin
Jenet
2020-08-25
csiro:d20f27c5-c6db-45cd-a213-3939e1900cbd
csiro:R-01211-01
csiro:PartyGroup
Topographic Position Index
LAND Topography Models
ECOLOGY Landscape
TERN_Soils
Land Surface
Australia
050399
050209
050205
050104
050302
Topographic Position Index (TPI) is a topographic position classification identifying upper, middle and lower parts of the landscape. This dataset includes a mask that identifies where topographic position cannot be reliably derived in low relief areas.
The TPI product was derived from Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 arc-second resolution SRTM data acquired by NASA in February 2000. A masked version of the TPI product was derived using the slope relief classification product.
The TPI data are available at 1 arc-second and 3 arc-second resolution.
The 3 arc-second resolution dataset was generated from the 1 arc-second TPI product and masked by the 3” water and ocean mask datasets.
Source data
1. 1 arc-second SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016).
2. 1 arc-second slope relief product
3. 3 arc-second resolution SRTM water body and ocean mask datasets.
Topographic position index calculation
TPI is a measure of topographic position, classified into three classes corresponding to upper slopes, mid-slopes and lower slopes. The method follows that of the "Drainage Channels Class" section of Warner, Cress and Sayre (2008) which is based on the TPI method of Jenness (2006) and Weiss (2001).
The TPI classification uses relative elevation as a fraction of local relief; where the relative elevation is high compared to the local relief the class is upper slope, and where the relative elevation is low compared to local relief the class is lower slope. Intermediate values are classified as mid-slopes. This use of residuals compared to a smoothed elevation model to produce relative elevations is similar to the method described by McRae (1992).
Relative elevation is the difference between local (cell) elevation and the mean elevation over a 300 m radius circle (approximately: the calculation actually uses 10 grid cells at 1 arc-second resolution). Local relief is calculated as the standard deviation of elevation over the same circular region. The classification is:
TPI = 1 if relative_elevation < -0.5 * local relief (lower slopes)
3 if relative_elevation > 0.5 * local relief (upper slopes)
2 otherwise (mid slopes)
In relatively flat areas the finite accuracy of a DEM limits its ability to discriminate topographic position. The mask included with the TPI layer identifies areas that are too flat to reliably identify upper, middle and lower landscape positions. It is based on the 'Slope-Relief' classification and the TPI mask has values of 1 where there is sufficient relief for TPI to be meaningful and 0 where TPI should not be used.
The TPI calculation was performed on 1° x 1° tiles, with overlaps to ensure correct values at tile edges.
The 3” arc-resolution version was generated from the 1” TPI class and mask products. This was done by aggregating the 1” data over a 3 x 3 grid cell window and taking the mean of the nine values that contributed to each 3” output grid cell. The result was then converted to integer format, avoiding truncation errors and ensuring that (for example) values between 1.5 and 2 were assigned to class 2, and values between 2.5 and 3 were assigned to class 3. The 3” TPI and TPI mask data were then masked using the SRTM 3” ocean and water body datasets.
All Rights (including copyright) CSIRO 2012.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10149
2022-11-10T22:57:26Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16111
https://data.csiro.au/dap/
102.100.100/16111
10.4225/08/546F29646877E
Soil and Landscape Grid National Soil Attribute Maps - Sand (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16111?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546F29646877E
Soil and Landscape Grid National Soil Attribute Maps - Sand (3" resolution) - Release 1
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_69
csiro:service_68
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Sand
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil Sand product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: 200 μm - 2 mm mass fraction of the less than 2 mm soil material determined using the pipette method;
Units: %;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:
1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a);
2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).
Version 1 of the National Digital Soil Property Maps combines mapping from the:
1) Australia-wide three-dimensional Digital Soil Property Maps;
2) Western Australia Polygon Disaggregation Maps;
3) South Australian Agricultural Areas Polygon Disaggregation Maps;
4) Tasmanian State-wide DSM Maps.
These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10168
2022-11-10T22:57:26Z
external_system_set_RDA
external_system_set_TERN_SOILS
102.100.100/16110
https://data.csiro.au/dap/
102.100.100/16110
10.4225/08/546EEE35164BF
Soil and Landscape Grid National Soil Attribute Maps - Clay (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16110?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546EEE35164BF
Soil and Landscape Grid National Soil Attribute Maps - Clay (3" resolution) - Release 1
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_63
csiro:service_64
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Clay
Continental
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil Clay product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: 2 μm mass fraction of the less than 2 mm soil material determined using the pipette method;
Units: %;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:
1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a);
2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).
Version 1 of the National Digital Soil Property Maps combines mapping from the:
1) Australia-wide three-dimensional Digital Soil Property Maps;
2) Western Australia Polygon Disaggregation Maps;
3) South Australian Agricultural Areas Polygon Disaggregation Maps;
4) Tasmanian State-wide DSM Maps.
These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10688
2022-11-10T22:57:26Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16112
https://data.csiro.au/dap/
102.100.100/16112
10.4225/08/546F48D6A6D48
Soil and Landscape Grid National Soil Attribute Maps - Silt (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16112?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546F48D6A6D48
Soil and Landscape Grid National Soil Attribute Maps - Silt (3" resolution) - Release 1
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_27
csiro:service_26
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Silt
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil Silt product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: 2-200 μm mass fraction of the less than 2 mm soil material determined using the pipette method;
Units: %;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:
1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a);
2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).
Version 1 of the National Digital Soil Property Maps combines mapping from the:
1) Australia-wide three-dimensional Digital Soil Property Maps;
2) Western Australia Polygon Disaggregation Maps;
3) South Australian Agricultural Areas Polygon Disaggregation Maps;
4) Tasmanian State-wide DSM Maps.
These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10516
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16127
https://data.csiro.au/dap/
102.100.100/16127
10.4225/08/5aaf39ed26044
Soil and Landscape Grid Digital Soil Property Maps for South Australia (3" resolution)
http://hdl.handle.net/102.100.100/16127?index=1
northlimit=-31.6073; southlimit=-38.061453; westlimit=131.6825; eastLimit=141.00134; uplimit=; downlimit=-2.0; projection=WGS84
10.4225/08/5aaf39ed26044
Soil and Landscape Grid Digital Soil Property Maps for South Australia (3" resolution)
v4
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Liddicoat
Craig
Holmes
Karen
Maschmedt
David
Rowland
Jan
Searle
Ross
Odgers
Nathan
2018-03-19
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
csiro:R-01211-01
csiro:PartyGroup
csiro:service_143
csiro:service_127
csiro:service_135
csiro:service_131
csiro:service_132
csiro:service_141
csiro:service_146
csiro:service_144
csiro:service_129
csiro:service_142
csiro:service_133
csiro:service_139
csiro:service_136
csiro:service_138
csiro:service_145
csiro:service_128
csiro:service_134
csiro:service_130
csiro:service_140
csiro:service_137
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
South Australia
DSM
Global Soil Map
spatial modelling
3-dimensional soil mapping
spatial uncertainty
DSMART
disaggregated
Available Water Capacity
Bulk Density
Bulk Density - Whole Earth
Cation Exchange Capacity
Clay
Coarse Fragments
Electrical Conductivity
Organic Carbon
pH - CaCl2
Sand
Silt
BD
pH
ECEC
EC
SLGA
050399
These products are derived from disaggregation of legacy soil mapping in the agricultural zone of South Australia using the DSMART tool (Odgers et al. 2014a); produced for the Soil and Landscape Grid of Australia Facility. There are 10 soil attribute products available from the Soil Facility: Available Water Capacity (AWC); Bulk Density - Whole Earth (BDw); Cation Exchange Capacity (CEC); Clay (CLY); Coarse Fragments (CFG); Electrical Conductivity (ECD); Organic Carbon (SOC); pH - CaCl2( pHc); Sand (SND); Silt (SLT).
Each soil attribute product is a collection of 6 depth slices (except for effective depth and total depth). Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the specifications of the GlobalSoilMap.
The DSMART tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes and corresponding soil properties. Legacy mapping was performed at 1:50,000 and 1:100,000 scales to delineate associated soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.
Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) kriging based on site data by Viscarra Rossel et al. (2014). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. 2014).
Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications.
The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.
Legacy soil mapping:
Polygon-based soil mapping for South Australia’s agricultural zone was developed via SA’s State Land and Soil Mapping Program (DEWNR 2014, Hall et al. 2009). Sixty one soil classes (termed ‘subgroup soils’) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).
Disaggregation of soil classes:
The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.
Soil property predictions:
The PROPR algorithm (Odgers et al. 2015b) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.
South Australia’s national- or ASRIS-format soil mapping was used to provide reference soil properties. This dataset was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties of approximately 1500 regional variants of the original sixty one ‘subgroup soil’ classes. Equal area depth smoothing splines were applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (2015a).
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10856
2018-03-19T06:18:40Z
external_system_set_RDA
external_system_set_TERN_SOILS
102.100.100/16122
https://data.csiro.au/dap/
102.100.100/16122
10.4225/08/5aaf364c54ccf
Soil and Landscape Grid Digital Soil Property Maps for Western Australia (3" resolution)
http://hdl.handle.net/102.100.100/16122?index=1
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; uplimit=; downlimit=-2.0; projection=WGS84
10.4225/08/5aaf364c54ccf
Soil and Landscape Grid Digital Soil Property Maps for Western Australia (3" resolution)
v4
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Holmes
Karen
Griffin
Ted
Odgers
Nathan
2018-03-19
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
csiro:R-01211-01
csiro:PartyGroup
csiro:service_103
csiro:service_107
csiro:service_96
csiro:service_102
csiro:service_109
csiro:service_97
csiro:service_100
csiro:service_90
csiro:service_99
csiro:service_93
csiro:service_110
csiro:service_105
csiro:service_108
csiro:service_91
csiro:service_104
csiro:service_101
csiro:service_106
csiro:service_98
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Western Australia
DSM
Global Soil Map
spatial modelling
3-dimensional soil mapping
spatial uncertainty
DSMART
disaggregated
Available Water Capacity
Bulk Density
Clay
Electrical Conductivity
Effective Cation Exchange Capacity
Coarse Fragments
Organic Carbon
pH
Sand
Silt
EC
ECEC
BD
pH - Water
SLGA
050399
These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).
Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.
The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.
Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).
Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications.
The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.
Legacy soil mapping:
Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).
Disaggregation of soil classes:
The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.
Soil property predictions:
The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.
Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:11379
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/19200
https://data.csiro.au/dap/
102.100.100/19200
10.4225/08/5aaf553b63215
Soil and Landscape Grid Australia-Wide 3D Soil Property Maps (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/19200?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/5aaf553b63215
Soil and Landscape Grid Australia-Wide 3D Soil Property Maps (3" resolution) - Release 1
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
2018-03-19
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_155
csiro:service_162
csiro:service_160
csiro:service_164
csiro:service_159
csiro:service_150
csiro:service_161
csiro:service_147
csiro:service_149
csiro:service_163
csiro:service_153
csiro:service_154
csiro:service_157
csiro:service_152
csiro:service_151
csiro:service_148
csiro:service_156
csiro:service_158
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Bulk Density
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
Effective Cation Exchange Capacity
Available Water Capacity
Bulk Density - Whole Earth
Clay
pH - CaCl2
Silt
Sand
Total Nitrogen
Total Phosphorus
ECEC
AWC
BD
pH
SLGA
050399
The Soil Facility produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Attributes included:
Available Water Capacity;
Bulk Density - Whole Earth;
Clay;
Effective Cation Exchange Capacity;
pH - CaCl2;
Silt;
Sand;
Total Nitrogen;
Total Phosphorus.
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 3.0 (CC By);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The digital soil attribute maps and their uncertainties were generated by harmonising different sources of soil data collected from point locations and using a 3-dimensional spatial modelling technique.
Soil inventory:
The national soil site data originates from two sources:
(i) A set collated with the assistance of all the Australian State and Territory soil agencies (Searle, 2014). The individual State soil databases were combined into a single database adhering to the NatSoil Site Schema (Jacquier et al., 2012). This database contains morphological and laboratory data for all the soil profiles publicly available within existing agency databases in 2013.
(ii) Spectroscopic estimates of the soil attributes with the Australian visible–near infrared database (Viscarra Rossel and Webster, 2012) on soil samples collected for the National Geochemical Survey of Australia (NGSA) (de Caritat, P & Cooper, M, 2011).
Harmonisation to standard depths:
Data for each soil attribute, for all depths that were present in the inventory, was extracted and harmonised to the six standard depths using two different methods. When there were data from more than two depths, a mass preserving spline (Bishop et al., 1999) was fitted to derive the standard depths. When only two depths were present we used the imputation method described by Clifford et al. (2014).
Spatial modelling:
The digital soil maps were generated by a 3-dimensional data mining-kriging approach with Monte Carlo resampling to produce estimates of uncertainty. The approach uses statistical relationships between the observed soil attributes at point locations and continuous values of more than 40 environmental covariates (including remote sensing, climatic data, a digital elevation model and terrain derivatives, gamma radiometrics and other geophysical data), and kriging of their residuals. The Cubist data mining software (Rulequest Research., 2008) implemented in the software R (R Core Team, 2013) was used for the data mining and the gstat package (Pebesma, 2004) was used for the geostatistical modelling. These hybrid models produce quantitative estimates of soil properties. Uncertainties in both parts of the model were quantified and expressed as the 90% confidence limits. Descriptions of the approach are given in Viscarra Rossel et al. (2015a); Viscarra Rossel and Chen (2011) and Viscarra Rossel, (2011).
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:11393
2018-03-16T08:38:54Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/19201
https://data.csiro.au/dap/
102.100.100/19201
10.4225/08/55C9472F05295
Soil and Landscape Grid National Soil Attribute Maps - Depth of Regolith (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/19201?index=1
1900-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/55C9472F05295
Soil and Landscape Grid National Soil Attribute Maps - Depth of Regolith (3" resolution) - Release 2
v6
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Wilford
John
Searle
Ross
Thomas
Mark
Grundy
Mike
2018-03-16
csiro:b9b8a4bd-e55b-4347-b641-c40b91d4f3ce
csiro:R-01211-01
csiro:PartyGroup
csiro:service_16
csiro:service_17
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Depth of Regolith
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 2 of the Depth of Regolith product of the Soil and Landscape Grid of Australia (produced 2015-06-01).
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Attribute Definition: The regolith is the in situ and transported material overlying unweathered bedrock;
Units: metres;
Spatial prediction method: data mining using piecewise linear regression;
Period (temporal coverage; approximately): 1900-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute:3;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Variance explained (cross-validation): R^2 = 0.38;
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The methodology consisted of the following steps: (i) drillhole data preparation, (ii) compilation and selection of the environmental covariate raster layers and (iii) model implementation and evaluation.
Drillhole data preparation:
Drillhole data was sourced from the National Groundwater Information System (NGIS) database. This spatial database holds nationally consistent information about bores that were drilled as part of the Bore Construction Licensing Framework (http://www.bom.gov.au/water/groundwater/ngis/). The database contains 357,834 bore locations with associated lithology, bore construction and hydrostratigraphy records. This information was loaded into a relational database to facilitate analysis.
Regolith depth extraction:
The first step was to recognise and extract the boundary between the regolith and bedrock within each drillhole record. This was done using a key word look-up table of bedrock or lithology related words from the record descriptions. 1,910 unique descriptors were discovered. Using this list of new standardised terms analysis of the drillholes was conducted, and the depth value associated with the word in the description that was unequivocally pointing to reaching fresh bedrock material was extracted from each record using a tool developed in C# code.
The second step of regolith depth extraction involved removal of drillhole bedrock depth records deemed necessary because of the “noisiness” in depth records resulting from inconsistencies we found in drilling and description standards indentified in the legacy database.
On completion of the filtering and removal of outliers the drillhole database used in the model comprised of 128,033 depth sites.
Selection and preparation of environmental covariates
The environmental correlations style of DSM applies environmental covariate datasets to predict target variables, here regolith depth. Strongly performing environmental covariates operate as proxies for the factors that control regolith formation including climate, relief, parent material organisms and time.
Depth modelling was implemented using the PC-based R-statistical software (R Core Team, 2014), and relied on the R-Cubist package (Kuhn et al. 2013). To generate modelling uncertainty estimates, the following procedures were followed: (i) the random withholding of a subset comprising 20% of the whole depth record dataset for external validation; (ii) Bootstrap sampling 100 times of the remaining dataset to produce repeated model training datasets, each time. The Cubist model was then run repeated times to produce a unique rule set for each of these training sets. Repeated model runs using different training sets, a procedure referred to as bagging or bootstrap aggregating, is a machine learning ensemble procedure designed to improve the stability and accuracy of the model. The Cubist rule sets generated were then evaluated and applied spatially calculating a mean predicted value (i.e. the final map). The 5% and 95% confidence intervals were estimated for each grid cell (pixel) in the prediction dataset by combining the variance from the bootstrapping process and the variance of the model residuals. Version 2 differs from version 1, in that the modelling of depths was performed on the log scale to better conform to assumptions of normality used in calculating the confidence intervals. The method to estimate the confidence intervals was improved to better represent the full range of variability in the modelling process. (Wilford et al, in press)
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2015.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:11413
2022-11-10T22:57:26Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/19205
https://data.csiro.au/dap/
102.100.100/19205
10.4225/08/546F540FE10AA
Soil and Landscape Grid National Soil Attribute Maps - Soil Depth (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/19205?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546F540FE10AA
Soil and Landscape Grid National Soil Attribute Maps - Soil Depth (3" resolution) - Release 1
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_29
csiro:service_28
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Soil Depth
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Soil Depth product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. This depth product estimates the depth of soil down to 2 metres. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
The soil attribute products are provided as continuous maps that represent each of six depth intervals to a maximum depth of 2 metres. We acknowledge that soil depth is variable across Australia, and in some landscapes there might be no soil or soil might be shallower than 2 metres. We have provided continuous maps because of the relative unavailability of data on soil depth. Further, existing data on depth is biased to near surface layers and there are few records that extend beyond 1.5m. Therefore, we provide here, our best estimate of soil depth to allow users to generate masks, which might be used together with the attribute maps to approximate the presence of areas with no soil or areas with shallow soil. We encourage users to draw on local data and expertise for such assessments.
Attribute Definition: Depth of soil profile (A & B horizons);
Units: metres;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 3;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel.
For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:11467
2022-11-10T22:52:22Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/19219
https://data.csiro.au/dap/
102.100.100/19219
10.4225/08/547523BB0801A
Soil and Landscape Grid National Soil Attribute Maps - Organic Carbon (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/19219?index=1
2000-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/547523BB0801A
Soil and Landscape Grid National Soil Attribute Maps - Organic Carbon (3" resolution) - Release 1
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_62
csiro:service_61
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Organic Carbon
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil Organic Carbon product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: Mass fraction of carbon by weight in the less than 2 mm soil material as determined by dry combustion at 900° C;
Units: %;
Period (temporal coverage; approximately): 2000-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being;
1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a);
2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).
Version 1 of the Australian Soil Property Maps combines mapping from the:
1) Australia-wide three-dimensional Digital Soil Property Maps;
2) South Australian Agricultural Areas Polygon Disaggregation Maps;
3) Tasmanian State-wide DSM Maps.
These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10877
2022-11-23T23:22:52Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16128
https://data.csiro.au/dap/
102.100.100/16128
10.4225/08/5aaf364c54cc8
Soil and Landscape Grid Digital Soil Property Maps for Tasmania (3" resolution)
http://hdl.handle.net/102.100.100/16128?index=1
1947-01-01
2014-09-30
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; uplimit=; downlimit=-2.0; projection=WGS84
10.4225/08/5aaf364c54cc8
Soil and Landscape Grid Digital Soil Property Maps for Tasmania (3" resolution)
v4
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Kidd
Darren
Webb
Mathew
Malone
Brendan
Minasnay
Budiman
McBratney
Alex
2018-03-19
csiro:48164382-5df9-483c-a22b-068112b92365
csiro:R-01211-01
csiro:PartyGroup
csiro:service_115
csiro:service_117
csiro:service_112
csiro:service_119
csiro:service_111
csiro:service_123
csiro:service_114
csiro:service_120
csiro:service_113
csiro:service_116
csiro:service_122
csiro:service_126
csiro:service_124
csiro:service_125
csiro:service_118
csiro:service_121
TERN_Soils
Soil
TERN
Raster
Attributes
Tasmania
DSM
Global Soil Map
spatial modelling
3-dimensional soil mapping
spatial uncertainty
Bulk Density
Bulk Density - Whole Earth
Clay
Coarse Fragments
Electrical Conductivity
Organic Carbon
pH - Water
Sand
Silt
BD
EC
pH
SLGA
050399
These are the soil attribute products of the Tasmanian Soil Attribute Grids. There are 8 soil attribute products available from the TERN Soil Facility. Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.
Attributes: pH - Water (pHw); Electical Conductivity dS/m (ECD); Clay % (CLY); Sand % (SND); Silt % (SLT); Bulk Density - Whole Earth Mg/m3 (BDw); Organic Carbon % (SOC); Coarse Fragments >2mm (CFG).
These products were developed using datasets held by the Tasmanian Department of Primary Industries Parks Water & Environment (DPIPWE) Soils Database. The mapping was made by using spatial modelling and digital soil mapping (DSM) techniques to produce a fine resolution 3 arc-second grid of soil attribute values and their uncertainties, across all of Tasmania.
Note: Previous versions of this collection contained a Depth layer. This has been removed as the units do not comply with Global Soil Map specifications.
The soil attribute maps are generated using spatial modelling and digital soil mapping techniques.
Soil inventory:
Tasmanian soil site data originates from the DPIPWE soils database, a compilation of various historical soil surveys undertaken by DPIPWE, CSIRO, Forestry Tasmania and the University of Tasmania.
This database contains morphological and laboratory data for all the soil sites.
Data Modelling :
A raster stack of all covariates was generated and the target variable (each soil property and depth) individually intersected with the covariate values to provide the calibration and validation data. All modelling was undertaken in ‘R’ (R Development Core Team 2012), using Regression tree (RT), specifically the Cubist R package (Kuhn, Weston et al. 2012; Kuhn, Weston et al. 2013; Quinlan 2005). The RT approach is a popular modelling approach for many disciplines (Breiman, Friedman et al. 1984), and has been widely used with DSM (Grunwald 2009; Kidd, Malone et al. 2014; McKenzie and Ryan 1999). Cubist develops the regression trees by first applying a data mining-approach to partition the calibration and explanatory covariate values into a set of structured ‘classifier’ data. The tree structure is developed by repeatedly partitioning the data into linear models until no significant measure of difference in the calibration data is determined (McBratney, Mendonça Santos et al. 2003). A series of covariate-based rules (conditions) is developed, and the linear model corresponding to the covariate conditions is applied to produce the final modelled surface. For this modelling exercise, the number of rules was set within the model controls to let the Cubist algorithm decide upon the optimum number of rules to generate.
Uncertainty
Leave-one-out-cross-validation (LOOCV) was applied to the Cubist model to generate rule-based uncertainties, using only those covariates forming the conditional partitioning of that rule, following Malone et al (2014). The LOOCV, applied to an individual Cubist model for each rule, effectively produced a mean value for each RT partition, with the upper and lower 5 and 95% quantiles of the prediction variation providing the lower and upper prediction uncertainty values respectively, at the 90% Prediction Interval (PI). A 10-fold cross validation was used to run this process 10 times across all data to produce mean modelling diagnostics and validations, and reduce modelling bias due to sensitivity to training data variance.
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO, Tasmania Department Primary Industries, Parks, Water and Environment 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10889
2018-03-16T05:27:52Z
external_system_set_RDA
external_system_set_TERN_SOILS
102.100.100/16113
https://data.csiro.au/dap/
102.100.100/16113
10.4225/08/546F564AE11F9
Soil and Landscape Grid National Soil Attribute Maps - Total Nitrogen (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16113?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546F564AE11F9
Soil and Landscape Grid National Soil Attribute Maps - Total Nitrogen (3" resolution) - Release 1
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_70
csiro:service_71
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Total Nitrogen
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil Total Nitrogen product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: Total nitrogen;
Units: %;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:10890
2018-03-16T05:22:47Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16120
https://data.csiro.au/dap/
102.100.100/16120
10.4225/08/546F617719CAF
Soil and Landscape Grid National Soil Attribute Maps - Total Phosphorus (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16120?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546F617719CAF
Soil and Landscape Grid National Soil Attribute Maps - Total Phosphorus (3" resolution) - Release 1
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_33
csiro:service_32
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Total Phosphorus
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil Total Phosphorus product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: Total phosphorus;
Units: %;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:11016
2022-11-10T22:57:26Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16129
https://data.csiro.au/dap/
102.100.100/16129
10.4225/08/546ED604ADD8A
Soil and Landscape Grid National Soil Attribute Maps - Available Water Capacity (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16129?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546ED604ADD8A
Soil and Landscape Grid National Soil Attribute Maps - Available Water Capacity (3" resolution) - Release 1
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_9
csiro:service_8
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Available Water Capacity
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
AWC
SLGA
050399
This is Version 1 of the Australian Soil Available Water Capacity product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: Available water capacity computed for each of the specified depth increments;
Units: %;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Variance explained (cross-validation): 0.4%;
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:11017
2018-03-16T08:33:49Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/16130
https://data.csiro.au/dap/
102.100.100/16130
10.4225/08/546F091C11777
Soil and Landscape Grid National Soil Attribute Maps - Effective Cation Exchange Capacity (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/16130?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546F091C11777
Soil and Landscape Grid National Soil Attribute Maps - Effective Cation Exchange Capacity (3" resolution) - Release 1
v4
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_19
csiro:service_18
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Effective Cation Exchange Capacity
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil Effective Cation Exchange Capacity product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: Cations extracted using barium chloride (BaCl2) plus exchangeable H + Al;
Units: meq/100g;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:11030
2018-03-16T07:58:35Z
external_system_set_RDA
external_system_set_TERN_SOILS
102.100.100/19204
https://data.csiro.au/dap/
102.100.100/19204
10.4225/08/546F17EC6AB6E
Soil and Landscape Grid National Soil Attribute Maps - pH - CaCl2 (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/19204?index=1
1950-01-01
2013-12-31
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; uplimit=0.0; downlimit=-2.0; projection=WGS84
10.4225/08/546F17EC6AB6E
Soil and Landscape Grid National Soil Attribute Maps - pH - CaCl2 (3" resolution) - Release 1
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Chen
Charlie
Grundy
Mike
Searle
Ross
Clifford
David
Odgers
Nathan
Holmes
Karen
Griffin
Ted
Liddicoat
Craig
Kidd
Darren
2018-03-16
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211-01
csiro:PartyGroup
csiro:service_23
csiro:service_67
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
pH - CaCl2
Continental
Australia
DSM
Global Soil Map
spatial modelling
visible-near infrared spectroscopy
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian Soil pH - CaCl2 product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.
Attribute Definition: pH of 1:5 soil/0.01M calcium chloride extract;
Units: None;
Period (temporal coverage; approximately): 1950-2013;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: GeoTIFF.
The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:
1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a);
2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).
Version 1 of the National Digital Soil Property Maps combines mapping from the:
1) Australia-wide three-dimensional Digital Soil Property Maps;
2) South Australian Agricultural Areas Polygon Disaggregation Maps.
These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
http://www.clw.csiro.au/aclep/soilandlandscapegrid/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
All Rights (including copyright) CSIRO 2014.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:service_8
2018-03-16T09:49:25Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_8
https://data.csiro.au/dap/
AWC_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
AWC_100_200_05_N_P_AU_NAT_C
layers
AWC_100_200_95_N_P_AU_NAT_C
layers
AWC_100_200_EV_N_P_AU_NAT_C
layers
AWC_060_100_05_N_P_AU_NAT_C
layers
AWC_060_100_95_N_P_AU_NAT_C
layers
AWC_060_100_EV_N_P_AU_NAT_C
layers
AWC_030_060_05_N_P_AU_NAT_C
layers
AWC_030_060_95_N_P_AU_NAT_C
layers
AWC_030_060_EV_N_P_AU_NAT_C
layers
AWC_015_030_05_N_P_AU_NAT_C
layers
AWC_015_030_95_N_P_AU_NAT_C
layers
AWC_015_030_EV_N_P_AU_NAT_C
layers
AWC_005_015_05_N_P_AU_NAT_C
layers
AWC_005_015_95_N_P_AU_NAT_C
layers
AWC_005_015_EV_N_P_AU_NAT_C
layers
AWC_000_005_05_N_P_AU_NAT_C
layers
AWC_000_005_95_N_P_AU_NAT_C
layers
AWC_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16129
Available Water Capacity is defined as the available water capacity computed for each of the specified depth increments. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_9
2018-03-16T09:49:25Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_9
https://data.csiro.au/dap/
AWC_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
AWC_000_005_EV_N_P_AU_NAT_C_1
AWC_000_005_95_N_P_AU_NAT_C_2
AWC_000_005_05_N_P_AU_NAT_C_3
AWC_005_015_EV_N_P_AU_NAT_C_4
AWC_005_015_95_N_P_AU_NAT_C_5
AWC_005_015_05_N_P_AU_NAT_C_6
AWC_015_030_EV_N_P_AU_NAT_C_7
AWC_015_030_95_N_P_AU_NAT_C_8
AWC_015_030_05_N_P_AU_NAT_C_9
AWC_030_060_EV_N_P_AU_NAT_C_10
AWC_030_060_95_N_P_AU_NAT_C_11
AWC_030_060_05_N_P_AU_NAT_C_12
AWC_060_100_EV_N_P_AU_NAT_C_13
AWC_060_100_95_N_P_AU_NAT_C_14
AWC_060_100_05_N_P_AU_NAT_C_15
AWC_100_200_EV_N_P_AU_NAT_C_16
AWC_100_200_95_N_P_AU_NAT_C_17
AWC_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16129
csiro:service_32
2018-03-16T05:22:47Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_32
https://data.csiro.au/dap/
PTO_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/PTO_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
PTO_000_005_EV_N_P_AU_NAT_C_1
PTO_000_005_95_N_P_AU_NAT_C_2
PTO_000_005_05_N_P_AU_NAT_C_3
PTO_005_015_EV_N_P_AU_NAT_C_4
PTO_005_015_95_N_P_AU_NAT_C_5
PTO_005_015_05_N_P_AU_NAT_C_6
PTO_015_030_EV_N_P_AU_NAT_C_7
PTO_015_030_95_N_P_AU_NAT_C_8
PTO_015_030_05_N_P_AU_NAT_C_9
PTO_030_060_EV_N_P_AU_NAT_C_10
PTO_030_060_95_N_P_AU_NAT_C_11
PTO_030_060_05_N_P_AU_NAT_C_12
PTO_060_100_EV_N_P_AU_NAT_C_13
PTO_060_100_95_N_P_AU_NAT_C_14
PTO_060_100_05_N_P_AU_NAT_C_15
PTO_100_200_EV_N_P_AU_NAT_C_16
PTO_100_200_95_N_P_AU_NAT_C_17
PTO_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16120
csiro:service_33
2018-03-16T05:22:47Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_33
https://data.csiro.au/dap/
PTO_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/PTO_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
PTO_100_200_05_N_P_AU_NAT_C
layers
PTO_100_200_95_N_P_AU_NAT_C
layers
PTO_100_200_EV_N_P_AU_NAT_C
layers
PTO_060_100_05_N_P_AU_NAT_C
layers
PTO_060_100_95_N_P_AU_NAT_C
layers
PTO_060_100_EV_N_P_AU_NAT_C
layers
PTO_030_060_05_N_P_AU_NAT_C
layers
PTO_030_060_95_N_P_AU_NAT_C
layers
PTO_030_060_EV_N_P_AU_NAT_C
layers
PTO_015_030_05_N_P_AU_NAT_C
layers
PTO_015_030_95_N_P_AU_NAT_C
layers
PTO_015_030_EV_N_P_AU_NAT_C
layers
PTO_005_015_05_N_P_AU_NAT_C
layers
PTO_005_015_95_N_P_AU_NAT_C
layers
PTO_005_015_EV_N_P_AU_NAT_C
layers
PTO_000_005_05_N_P_AU_NAT_C
layers
PTO_000_005_95_N_P_AU_NAT_C
layers
PTO_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16120
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_28
2018-03-16T05:27:52Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_28
https://data.csiro.au/dap/
DES_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/DES_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
DES_000_200_95_N_P_AU_NAT_C
layers
DES_000_200_05_N_P_AU_NAT_C
layers
DES_000_200_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19205
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm &amp; 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_29
2018-03-16T05:27:52Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_29
https://data.csiro.au/dap/
DES_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/DES_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
DES_000_200_EV_N_P_AU_NAT_C_1
DES_000_200_05_N_P_AU_NAT_C_2
DES_000_200_95_N_P_AU_NAT_C_3
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19205
csiro:service_70
2018-03-16T05:27:52Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_70
https://data.csiro.au/dap/
NTO_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/NTO_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
NTO_000_005_EV_N_P_AU_NAT_C_1
NTO_000_005_95_N_P_AU_NAT_C_2
NTO_000_005_05_N_P_AU_NAT_C_3
NTO_005_015_EV_N_P_AU_NAT_C_4
NTO_005_015_95_N_P_AU_NAT_C_5
NTO_005_015_05_N_P_AU_NAT_C_6
NTO_015_030_EV_N_P_AU_NAT_C_7
NTO_015_030_95_N_P_AU_NAT_C_8
NTO_015_030_05_N_P_AU_NAT_C_9
NTO_030_060_EV_N_P_AU_NAT_C_10
NTO_030_060_95_N_P_AU_NAT_C_11
NTO_030_060_05_N_P_AU_NAT_C_12
NTO_060_100_EV_N_P_AU_NAT_C_13
NTO_060_100_95_N_P_AU_NAT_C_14
NTO_060_100_05_N_P_AU_NAT_C_15
NTO_100_200_EV_N_P_AU_NAT_C_16
NTO_100_200_95_N_P_AU_NAT_C_17
NTO_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16113
csiro:service_71
2018-03-16T05:27:52Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_71
https://data.csiro.au/dap/
NTO_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/NTO_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
NTO_100_200_05_N_P_AU_NAT_C
layers
NTO_100_200_95_N_P_AU_NAT_C
layers
NTO_100_200_EV_N_P_AU_NAT_C
layers
NTO_060_100_05_N_P_AU_NAT_C
layers
NTO_060_100_95_N_P_AU_NAT_C
layers
NTO_060_100_EV_N_P_AU_NAT_C
layers
NTO_030_060_05_N_P_AU_NAT_C
layers
NTO_030_060_95_N_P_AU_NAT_C
layers
NTO_030_060_EV_N_P_AU_NAT_C
layers
NTO_015_030_05_N_P_AU_NAT_C
layers
NTO_015_030_95_N_P_AU_NAT_C
layers
NTO_015_030_EV_N_P_AU_NAT_C
layers
NTO_005_015_05_N_P_AU_NAT_C
layers
NTO_005_015_95_N_P_AU_NAT_C
layers
NTO_005_015_EV_N_P_AU_NAT_C
layers
NTO_000_005_05_N_P_AU_NAT_C
layers
NTO_000_005_95_N_P_AU_NAT_C
layers
NTO_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16113
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_26
2018-03-16T05:58:07Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_26
https://data.csiro.au/dap/
SLT_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SLT_100_200_05_N_P_AU_NAT_C
layers
SLT_100_200_95_N_P_AU_NAT_C
layers
SLT_100_200_EV_N_P_AU_NAT_C
layers
SLT_060_100_05_N_P_AU_NAT_C
layers
SLT_060_100_95_N_P_AU_NAT_C
layers
SLT_060_100_EV_N_P_AU_NAT_C
layers
SLT_030_060_05_N_P_AU_NAT_C
layers
SLT_030_060_95_N_P_AU_NAT_C
layers
SLT_030_060_EV_N_P_AU_NAT_C
layers
SLT_015_030_05_N_P_AU_NAT_C
layers
SLT_015_030_95_N_P_AU_NAT_C
layers
SLT_015_030_EV_N_P_AU_NAT_C
layers
SLT_005_015_05_N_P_AU_NAT_C
layers
SLT_005_015_95_N_P_AU_NAT_C
layers
SLT_005_015_EV_N_P_AU_NAT_C
layers
SLT_000_005_05_N_P_AU_NAT_C
layers
SLT_000_005_95_N_P_AU_NAT_C
layers
SLT_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16112
Silt content is defined as the 2 - 20 um mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_27
2018-03-16T05:58:07Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_27
https://data.csiro.au/dap/
SLT_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SLT_000_005_EV_N_P_AU_NAT_C_1
SLT_000_005_95_N_P_AU_NAT_C_2
SLT_000_005_05_N_P_AU_NAT_C_3
SLT_005_015_EV_N_P_AU_NAT_C_4
SLT_005_015_95_N_P_AU_NAT_C_5
SLT_005_015_05_N_P_AU_NAT_C_6
SLT_015_030_EV_N_P_AU_NAT_C_7
SLT_015_030_95_N_P_AU_NAT_C_8
SLT_015_030_05_N_P_AU_NAT_C_9
SLT_030_060_EV_N_P_AU_NAT_C_10
SLT_030_060_95_N_P_AU_NAT_C_11
SLT_030_060_05_N_P_AU_NAT_C_12
SLT_060_100_EV_N_P_AU_NAT_C_13
SLT_060_100_95_N_P_AU_NAT_C_14
SLT_060_100_05_N_P_AU_NAT_C_15
SLT_100_200_EV_N_P_AU_NAT_C_16
SLT_100_200_95_N_P_AU_NAT_C_17
SLT_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670142; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16112
csiro:service_23
2018-03-16T07:58:35Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_23
https://data.csiro.au/dap/
PHC_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/PHC_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
PHC_000_005_EV_N_P_AU_NAT_C_1
PHC_000_005_95_N_P_AU_NAT_C_2
PHC_000_005_05_N_P_AU_NAT_C_3
PHC_005_015_EV_N_P_AU_NAT_C_4
PHC_005_015_95_N_P_AU_NAT_C_5
PHC_005_015_05_N_P_AU_NAT_C_6
PHC_015_030_EV_N_P_AU_NAT_C_7
PHC_015_030_95_N_P_AU_NAT_C_8
PHC_015_030_05_N_P_AU_NAT_C_9
PHC_030_060_EV_N_P_AU_NAT_C_10
PHC_030_060_95_N_P_AU_NAT_C_11
PHC_030_060_05_N_P_AU_NAT_C_12
PHC_060_100_EV_N_P_AU_NAT_C_13
PHC_060_100_95_N_P_AU_NAT_C_14
PHC_060_100_05_N_P_AU_NAT_C_15
PHC_100_200_EV_N_P_AU_NAT_C_16
PHC_100_200_95_N_P_AU_NAT_C_17
PHC_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670142; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19204
csiro:service_67
2018-03-16T07:58:35Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_67
https://data.csiro.au/dap/
PHC_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/PHC_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
PHC_100_200_05_N_P_AU_NAT_C
layers
PHC_100_200_95_N_P_AU_NAT_C
layers
PHC_100_200_EV_N_P_AU_NAT_C
layers
PHC_060_100_05_N_P_AU_NAT_C
layers
PHC_060_100_95_N_P_AU_NAT_C
layers
PHC_060_100_EV_N_P_AU_NAT_C
layers
PHC_030_060_05_N_P_AU_NAT_C
layers
PHC_030_060_95_N_P_AU_NAT_C
layers
PHC_030_060_EV_N_P_AU_NAT_C
layers
PHC_015_030_05_N_P_AU_NAT_C
layers
PHC_015_030_95_N_P_AU_NAT_C
layers
PHC_015_030_EV_N_P_AU_NAT_C
layers
PHC_005_015_05_N_P_AU_NAT_C
layers
PHC_005_015_95_N_P_AU_NAT_C
layers
PHC_005_015_EV_N_P_AU_NAT_C
layers
PHC_000_005_05_N_P_AU_NAT_C
layers
PHC_000_005_95_N_P_AU_NAT_C
layers
PHC_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19204
pH in Calcium Chloride is defined as the of 1:5 soil/0.01M calcium chloride extract. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_68
2018-03-16T07:58:35Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_68
https://data.csiro.au/dap/
SND_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SND_000_005_EV_N_P_AU_NAT_C_1
SND_000_005_95_N_P_AU_NAT_C_2
SND_000_005_05_N_P_AU_NAT_C_3
SND_005_015_EV_N_P_AU_NAT_C_4
SND_005_015_95_N_P_AU_NAT_C_5
SND_005_015_05_N_P_AU_NAT_C_6
SND_015_030_EV_N_P_AU_NAT_C_7
SND_015_030_95_N_P_AU_NAT_C_8
SND_015_030_05_N_P_AU_NAT_C_9
SND_030_060_EV_N_P_AU_NAT_C_10
SND_030_060_95_N_P_AU_NAT_C_11
SND_030_060_05_N_P_AU_NAT_C_12
SND_060_100_EV_N_P_AU_NAT_C_13
SND_060_100_95_N_P_AU_NAT_C_14
SND_060_100_05_N_P_AU_NAT_C_15
SND_100_200_EV_N_P_AU_NAT_C_16
SND_100_200_95_N_P_AU_NAT_C_17
SND_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670142; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16111
csiro:service_69
2018-03-16T07:58:35Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_69
https://data.csiro.au/dap/
SND_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SND_100_200_05_N_P_AU_NAT_C
layers
SND_100_200_95_N_P_AU_NAT_C
layers
SND_100_200_EV_N_P_AU_NAT_C
layers
SND_060_100_05_N_P_AU_NAT_C
layers
SND_060_100_95_N_P_AU_NAT_C
layers
SND_060_100_EV_N_P_AU_NAT_C
layers
SND_030_060_05_N_P_AU_NAT_C
layers
SND_030_060_95_N_P_AU_NAT_C
layers
SND_030_060_EV_N_P_AU_NAT_C
layers
SND_015_030_05_N_P_AU_NAT_C
layers
SND_015_030_95_N_P_AU_NAT_C
layers
SND_015_030_EV_N_P_AU_NAT_C
layers
SND_005_015_05_N_P_AU_NAT_C
layers
SND_005_015_95_N_P_AU_NAT_C
layers
SND_005_015_EV_N_P_AU_NAT_C
layers
SND_000_005_05_N_P_AU_NAT_C
layers
SND_000_005_95_N_P_AU_NAT_C
layers
SND_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16111
Sand content is defined as the 20 um - 2 mm mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_61
2018-03-16T08:28:45Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_61
https://data.csiro.au/dap/
SOC_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/SOC_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SOC_100_200_05_N_P_AU_NAT_C
layers
SOC_100_200_95_N_P_AU_NAT_C
layers
SOC_100_200_EV_N_P_AU_NAT_C
layers
SOC_060_100_05_N_P_AU_NAT_C
layers
SOC_060_100_95_N_P_AU_NAT_C
layers
SOC_060_100_EV_N_P_AU_NAT_C
layers
SOC_030_060_05_N_P_AU_NAT_C
layers
SOC_030_060_95_N_P_AU_NAT_C
layers
SOC_030_060_EV_N_P_AU_NAT_C
layers
SOC_015_030_05_N_P_AU_NAT_C
layers
SOC_015_030_95_N_P_AU_NAT_C
layers
SOC_015_030_EV_N_P_AU_NAT_C
layers
SOC_005_015_05_N_P_AU_NAT_C
layers
SOC_005_015_95_N_P_AU_NAT_C
layers
SOC_005_015_EV_N_P_AU_NAT_C
layers
SOC_000_005_05_N_P_AU_NAT_C
layers
SOC_000_005_95_N_P_AU_NAT_C
layers
SOC_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19219
Organic Carbon is defined as the mass fraction of carbon by weight in the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_62
2018-03-16T08:28:45Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_62
https://data.csiro.au/dap/
SOC_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/SOC_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SOC_000_005_EV_N_P_AU_NAT_C_1
SOC_000_005_95_N_P_AU_NAT_C_2
SOC_000_005_05_N_P_AU_NAT_C_3
SOC_005_015_EV_N_P_AU_NAT_C_4
SOC_005_015_95_N_P_AU_NAT_C_5
SOC_005_015_05_N_P_AU_NAT_C_6
SOC_015_030_EV_N_P_AU_NAT_C_7
SOC_015_030_95_N_P_AU_NAT_C_8
SOC_015_030_05_N_P_AU_NAT_C_9
SOC_030_060_EV_N_P_AU_NAT_C_10
SOC_030_060_95_N_P_AU_NAT_C_11
SOC_030_060_05_N_P_AU_NAT_C_12
SOC_060_100_EV_N_P_AU_NAT_C_13
SOC_060_100_95_N_P_AU_NAT_C_14
SOC_060_100_05_N_P_AU_NAT_C_15
SOC_100_200_EV_N_P_AU_NAT_C_16
SOC_100_200_95_N_P_AU_NAT_C_17
SOC_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19219
csiro:service_18
2018-03-16T08:33:49Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_18
https://data.csiro.au/dap/
ECE_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/ECE_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
ECE_000_005_EV_N_P_AU_NAT_C_1
ECE_000_005_95_N_P_AU_NAT_C_2
ECE_000_005_05_N_P_AU_NAT_C_3
ECE_005_015_EV_N_P_AU_NAT_C_4
ECE_005_015_95_N_P_AU_NAT_C_5
ECE_005_015_05_N_P_AU_NAT_C_6
ECE_015_030_EV_N_P_AU_NAT_C_7
ECE_015_030_95_N_P_AU_NAT_C_8
ECE_015_030_05_N_P_AU_NAT_C_9
ECE_030_060_EV_N_P_AU_NAT_C_10
ECE_030_060_95_N_P_AU_NAT_C_11
ECE_030_060_05_N_P_AU_NAT_C_12
ECE_060_100_EV_N_P_AU_NAT_C_13
ECE_060_100_95_N_P_AU_NAT_C_14
ECE_060_100_05_N_P_AU_NAT_C_15
ECE_100_200_EV_N_P_AU_NAT_C_16
ECE_100_200_95_N_P_AU_NAT_C_17
ECE_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16130
csiro:service_19
2018-03-16T08:33:49Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_19
https://data.csiro.au/dap/
ECE_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/ECE_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
ECE_100_200_05_N_P_AU_NAT_C
layers
ECE_100_200_95_N_P_AU_NAT_C
layers
ECE_100_200_EV_N_P_AU_NAT_C
layers
ECE_060_100_05_N_P_AU_NAT_C
layers
ECE_060_100_95_N_P_AU_NAT_C
layers
ECE_060_100_EV_N_P_AU_NAT_C
layers
ECE_030_060_05_N_P_AU_NAT_C
layers
ECE_030_060_95_N_P_AU_NAT_C
layers
ECE_030_060_EV_N_P_AU_NAT_C
layers
ECE_015_030_05_N_P_AU_NAT_C
layers
ECE_015_030_95_N_P_AU_NAT_C
layers
ECE_015_030_EV_N_P_AU_NAT_C
layers
ECE_005_015_05_N_P_AU_NAT_C
layers
ECE_005_015_95_N_P_AU_NAT_C
layers
ECE_005_015_EV_N_P_AU_NAT_C
layers
ECE_000_005_05_N_P_AU_NAT_C
layers
ECE_000_005_95_N_P_AU_NAT_C
layers
ECE_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16130
Effective Cation Exchange Capacity is defined as the concentration of cations extracted using barium chloride (BaCl2) plus exchangeable H + Al. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_16
2018-03-16T08:38:54Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_16
https://data.csiro.au/dap/
DER_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/DER_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
DER_000_999_EV_N_P_AU_NAT_C_1
DER_000_999_05_N_P_AU_NAT_C_2
DER_000_999_95_N_P_AU_NAT_C_3
northlimit=-10.0004166664663; southlimit=-44.0004166670142; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:b9b8a4bd-e55b-4347-b641-c40b91d4f3ce
102.100.100/19201
csiro:service_17
2018-03-16T08:38:54Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_17
https://data.csiro.au/dap/
WMS
http://www.asris.csiro.au/arcgis/services/TERN/DER_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
DER_000_999_95_N_P_AU_NAT_C
layers
DER_000_999_05_N_P_AU_NAT_C
layers
DER_000_999_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:b9b8a4bd-e55b-4347-b641-c40b91d4f3ce
102.100.100/19201
WMS
csiro:service_12
2018-03-16T09:34:18Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_12
https://data.csiro.au/dap/
BDW_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
BDW_100_200_05_N_P_AU_NAT_C
layers
BDW_100_200_95_N_P_AU_NAT_C
layers
BDW_100_200_EV_N_P_AU_NAT_C
layers
BDW_060_100_05_N_P_AU_NAT_C
layers
BDW_060_100_95_N_P_AU_NAT_C
layers
BDW_060_100_EV_N_P_AU_NAT_C
layers
BDW_030_060_05_N_P_AU_NAT_C
layers
BDW_030_060_95_N_P_AU_NAT_C
layers
BDW_030_060_EV_N_P_AU_NAT_C
layers
BDW_015_030_05_N_P_AU_NAT_C
layers
BDW_015_030_95_N_P_AU_NAT_C
layers
BDW_015_030_EV_N_P_AU_NAT_C
layers
BDW_005_015_05_N_P_AU_NAT_C
layers
BDW_005_015_95_N_P_AU_NAT_C
layers
BDW_005_015_EV_N_P_AU_NAT_C
layers
BDW_000_005_05_N_P_AU_NAT_C
layers
BDW_000_005_95_N_P_AU_NAT_C
layers
BDW_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16109
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_13
2018-03-16T09:34:18Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_13
https://data.csiro.au/dap/
BDW_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
BDW_000_005_EV_N_P_AU_NAT_C_1
BDW_000_005_95_N_P_AU_NAT_C_2
BDW_000_005_05_N_P_AU_NAT_C_3
BDW_005_015_EV_N_P_AU_NAT_C_4
BDW_005_015_95_N_P_AU_NAT_C_5
BDW_005_015_05_N_P_AU_NAT_C_6
BDW_015_030_EV_N_P_AU_NAT_C_7
BDW_015_030_95_N_P_AU_NAT_C_8
BDW_015_030_05_N_P_AU_NAT_C_9
BDW_030_060_EV_N_P_AU_NAT_C_10
BDW_030_060_95_N_P_AU_NAT_C_11
BDW_030_060_05_N_P_AU_NAT_C_12
BDW_060_100_EV_N_P_AU_NAT_C_13
BDW_060_100_95_N_P_AU_NAT_C_14
BDW_060_100_05_N_P_AU_NAT_C_15
BDW_100_200_EV_N_P_AU_NAT_C_16
BDW_100_200_95_N_P_AU_NAT_C_17
BDW_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670142; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16109
csiro:service_63
2018-03-16T09:34:18Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_63
https://data.csiro.au/dap/
CLY_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_NAT_C/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CLY_100_200_05_N_P_AU_NAT_C
layers
CLY_100_200_95_N_P_AU_NAT_C
layers
CLY_100_200_EV_N_P_AU_NAT_C
layers
CLY_060_100_05_N_P_AU_NAT_C
layers
CLY_060_100_95_N_P_AU_NAT_C
layers
CLY_060_100_EV_N_P_AU_NAT_C
layers
CLY_030_060_05_N_P_AU_NAT_C
layers
CLY_030_060_95_N_P_AU_NAT_C
layers
CLY_030_060_EV_N_P_AU_NAT_C
layers
CLY_015_030_05_N_P_AU_NAT_C
layers
CLY_015_030_95_N_P_AU_NAT_C
layers
CLY_015_030_EV_N_P_AU_NAT_C
layers
CLY_005_015_05_N_P_AU_NAT_C
layers
CLY_005_015_95_N_P_AU_NAT_C
layers
CLY_005_015_EV_N_P_AU_NAT_C
layers
CLY_000_005_05_N_P_AU_NAT_C
layers
CLY_000_005_95_N_P_AU_NAT_C
layers
CLY_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16110
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_64
2018-03-16T09:34:18Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_64
https://data.csiro.au/dap/
CLY_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_NAT_C/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CLY_000_005_EV_N_P_AU_NAT_C_1
CLY_000_005_95_N_P_AU_NAT_C_2
CLY_000_005_05_N_P_AU_NAT_C_3
CLY_005_015_EV_N_P_AU_NAT_C_4
CLY_005_015_95_N_P_AU_NAT_C_5
CLY_005_015_05_N_P_AU_NAT_C_6
CLY_015_030_EV_N_P_AU_NAT_C_7
CLY_015_030_95_N_P_AU_NAT_C_8
CLY_015_030_05_N_P_AU_NAT_C_9
CLY_030_060_EV_N_P_AU_NAT_C_10
CLY_030_060_95_N_P_AU_NAT_C_11
CLY_030_060_05_N_P_AU_NAT_C_12
CLY_060_100_EV_N_P_AU_NAT_C_13
CLY_060_100_95_N_P_AU_NAT_C_14
CLY_060_100_05_N_P_AU_NAT_C_15
CLY_100_200_EV_N_P_AU_NAT_C_16
CLY_100_200_95_N_P_AU_NAT_C_17
CLY_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670142; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16110
csiro:service_100
2018-03-19T06:18:40Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_100
https://data.csiro.au/dap/
CFG_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CFG_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CFG_100_200_05_N_P_AU_WAT_D
layers
CFG_100_200_95_N_P_AU_WAT_D
layers
CFG_100_200_EV_N_P_AU_WAT_D
layers
CFG_060_100_05_N_P_AU_WAT_D
layers
CFG_060_100_95_N_P_AU_WAT_D
layers
CFG_060_100_EV_N_P_AU_WAT_D
layers
CFG_030_060_05_N_P_AU_WAT_D
layers
CFG_030_060_95_N_P_AU_WAT_D
layers
CFG_030_060_EV_N_P_AU_WAT_D
layers
CFG_015_030_05_N_P_AU_WAT_D
layers
CFG_015_030_95_N_P_AU_WAT_D
layers
CFG_015_030_EV_N_P_AU_WAT_D
layers
CFG_005_015_05_N_P_AU_WAT_D
layers
CFG_005_015_95_N_P_AU_WAT_D
layers
CFG_005_015_EV_N_P_AU_WAT_D
layers
CFG_000_005_05_N_P_AU_WAT_D
layers
CFG_000_005_95_N_P_AU_WAT_D
layers
CFG_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
Coarse Fragments is defined as the mass fraction of the soil material greater than 2 mm. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_101
2018-03-19T06:18:40Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_101
https://data.csiro.au/dap/
BDW_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
BDW_100_200_05_N_P_AU_WAT_D
layers
BDW_100_200_95_N_P_AU_WAT_D
layers
BDW_100_200_EV_N_P_AU_WAT_D
layers
BDW_060_100_05_N_P_AU_WAT_D
layers
BDW_060_100_95_N_P_AU_WAT_D
layers
BDW_060_100_EV_N_P_AU_WAT_D
layers
BDW_030_060_05_N_P_AU_WAT_D
layers
BDW_030_060_95_N_P_AU_WAT_D
layers
BDW_030_060_EV_N_P_AU_WAT_D
layers
BDW_015_030_05_N_P_AU_WAT_D
layers
BDW_015_030_95_N_P_AU_WAT_D
layers
BDW_015_030_EV_N_P_AU_WAT_D
layers
BDW_005_015_05_N_P_AU_WAT_D
layers
BDW_005_015_95_N_P_AU_WAT_D
layers
BDW_005_015_EV_N_P_AU_WAT_D
layers
BDW_000_005_05_N_P_AU_WAT_D
layers
BDW_000_005_95_N_P_AU_WAT_D
layers
BDW_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
Bulk Density (Whole Earth) is defined as the Bulk Density of the whole soil (including coarse fragments) in mass per unit volume. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_102
2018-03-19T06:18:39Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_102
https://data.csiro.au/dap/
BDF_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/BDF_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
BDF_000_005_EV_N_P_AU_WAT_D_1
BDF_000_005_95_N_P_AU_WAT_D_2
BDF_000_005_05_N_P_AU_WAT_D_3
BDF_005_015_EV_N_P_AU_WAT_D_4
BDF_005_015_95_N_P_AU_WAT_D_5
BDF_005_015_05_N_P_AU_WAT_D_6
BDF_015_030_EV_N_P_AU_WAT_D_7
BDF_015_030_95_N_P_AU_WAT_D_8
BDF_015_030_05_N_P_AU_WAT_D_9
BDF_030_060_EV_N_P_AU_WAT_D_10
BDF_030_060_95_N_P_AU_WAT_D_11
BDF_030_060_05_N_P_AU_WAT_D_12
BDF_060_100_EV_N_P_AU_WAT_D_13
BDF_060_100_95_N_P_AU_WAT_D_14
BDF_060_100_05_N_P_AU_WAT_D_15
BDF_100_200_EV_N_P_AU_WAT_D_16
BDF_100_200_95_N_P_AU_WAT_D_17
BDF_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_103
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_103
https://data.csiro.au/dap/
ECD_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/ECD_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
ECD_000_005_EV_N_P_AU_WAT_D_1
ECD_000_005_95_N_P_AU_WAT_D_2
ECD_000_005_05_N_P_AU_WAT_D_3
ECD_005_015_EV_N_P_AU_WAT_D_4
ECD_005_015_95_N_P_AU_WAT_D_5
ECD_005_015_05_N_P_AU_WAT_D_6
ECD_015_030_EV_N_P_AU_WAT_D_7
ECD_015_030_95_N_P_AU_WAT_D_8
ECD_015_030_05_N_P_AU_WAT_D_9
ECD_030_060_EV_N_P_AU_WAT_D_10
ECD_030_060_95_N_P_AU_WAT_D_11
ECD_030_060_05_N_P_AU_WAT_D_12
ECD_060_100_EV_N_P_AU_WAT_D_13
ECD_060_100_95_N_P_AU_WAT_D_14
ECD_060_100_05_N_P_AU_WAT_D_15
ECD_100_200_EV_N_P_AU_WAT_D_16
ECD_100_200_95_N_P_AU_WAT_D_17
ECD_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_104
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_104
https://data.csiro.au/dap/
BDF_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/BDF_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
BDF_100_200_05_N_P_AU_WAT_D
layers
BDF_100_200_95_N_P_AU_WAT_D
layers
BDF_100_200_EV_N_P_AU_WAT_D
layers
BDF_060_100_05_N_P_AU_WAT_D
layers
BDF_060_100_95_N_P_AU_WAT_D
layers
BDF_060_100_EV_N_P_AU_WAT_D
layers
BDF_030_060_05_N_P_AU_WAT_D
layers
BDF_030_060_95_N_P_AU_WAT_D
layers
BDF_030_060_EV_N_P_AU_WAT_D
layers
BDF_015_030_05_N_P_AU_WAT_D
layers
BDF_015_030_95_N_P_AU_WAT_D
layers
BDF_015_030_EV_N_P_AU_WAT_D
layers
BDF_005_015_05_N_P_AU_WAT_D
layers
BDF_005_015_95_N_P_AU_WAT_D
layers
BDF_005_015_EV_N_P_AU_WAT_D
layers
BDF_000_005_05_N_P_AU_WAT_D
layers
BDF_000_005_95_N_P_AU_WAT_D
layers
BDF_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
Bulk Density (Fine Earth) is defined as the Bulk Density of the fine earth (excluding coarse fragments) in mass per unit volume. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_105
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_105
https://data.csiro.au/dap/
AWC_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
AWC_100_200_05_N_P_AU_WAT_D
layers
AWC_100_200_95_N_P_AU_WAT_D
layers
AWC_100_200_EV_N_P_AU_WAT_D
layers
AWC_060_100_05_N_P_AU_WAT_D
layers
AWC_060_100_95_N_P_AU_WAT_D
layers
AWC_060_100_EV_N_P_AU_WAT_D
layers
AWC_030_060_05_N_P_AU_WAT_D
layers
AWC_030_060_95_N_P_AU_WAT_D
layers
AWC_030_060_EV_N_P_AU_WAT_D
layers
AWC_015_030_05_N_P_AU_WAT_D
layers
AWC_015_030_95_N_P_AU_WAT_D
layers
AWC_015_030_EV_N_P_AU_WAT_D
layers
AWC_005_015_05_N_P_AU_WAT_D
layers
AWC_005_015_95_N_P_AU_WAT_D
layers
AWC_005_015_EV_N_P_AU_WAT_D
layers
AWC_000_005_05_N_P_AU_WAT_D
layers
AWC_000_005_95_N_P_AU_WAT_D
layers
AWC_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
Available Water Capacity is defined as the available water capacity computed for each of the specified depth increments. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_106
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_106
https://data.csiro.au/dap/
CLY_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CLY_100_200_05_N_P_AU_WAT_D
layers
CLY_100_200_95_N_P_AU_WAT_D
layers
CLY_100_200_EV_N_P_AU_WAT_D
layers
CLY_060_100_05_N_P_AU_WAT_D
layers
CLY_060_100_95_N_P_AU_WAT_D
layers
CLY_060_100_EV_N_P_AU_WAT_D
layers
CLY_030_060_05_N_P_AU_WAT_D
layers
CLY_030_060_95_N_P_AU_WAT_D
layers
CLY_030_060_EV_N_P_AU_WAT_D
layers
CLY_015_030_05_N_P_AU_WAT_D
layers
CLY_015_030_95_N_P_AU_WAT_D
layers
CLY_015_030_EV_N_P_AU_WAT_D
layers
CLY_005_015_05_N_P_AU_WAT_D
layers
CLY_005_015_95_N_P_AU_WAT_D
layers
CLY_005_015_EV_N_P_AU_WAT_D
layers
CLY_000_005_05_N_P_AU_WAT_D
layers
CLY_000_005_95_N_P_AU_WAT_D
layers
CLY_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_107
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_107
https://data.csiro.au/dap/
CLY_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CLY_000_005_EV_N_P_AU_WAT_D_1
CLY_000_005_95_N_P_AU_WAT_D_2
CLY_000_005_05_N_P_AU_WAT_D_3
CLY_005_015_EV_N_P_AU_WAT_D_4
CLY_005_015_95_N_P_AU_WAT_D_5
CLY_005_015_05_N_P_AU_WAT_D_6
CLY_015_030_EV_N_P_AU_WAT_D_7
CLY_015_030_95_N_P_AU_WAT_D_8
CLY_015_030_05_N_P_AU_WAT_D_9
CLY_030_060_EV_N_P_AU_WAT_D_10
CLY_030_060_95_N_P_AU_WAT_D_11
CLY_030_060_05_N_P_AU_WAT_D_12
CLY_060_100_EV_N_P_AU_WAT_D_13
CLY_060_100_95_N_P_AU_WAT_D_14
CLY_060_100_05_N_P_AU_WAT_D_15
CLY_100_200_EV_N_P_AU_WAT_D_16
CLY_100_200_95_N_P_AU_WAT_D_17
CLY_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_108
2018-03-19T06:18:39Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_108
https://data.csiro.au/dap/
BDW_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
BDW_000_005_EV_N_P_AU_WAT_D_1
BDW_000_005_95_N_P_AU_WAT_D_2
BDW_000_005_05_N_P_AU_WAT_D_3
BDW_005_015_EV_N_P_AU_WAT_D_4
BDW_005_015_95_N_P_AU_WAT_D_5
BDW_005_015_05_N_P_AU_WAT_D_6
BDW_015_030_EV_N_P_AU_WAT_D_7
BDW_015_030_95_N_P_AU_WAT_D_8
BDW_015_030_05_N_P_AU_WAT_D_9
BDW_030_060_EV_N_P_AU_WAT_D_10
BDW_030_060_95_N_P_AU_WAT_D_11
BDW_030_060_05_N_P_AU_WAT_D_12
BDW_060_100_EV_N_P_AU_WAT_D_13
BDW_060_100_95_N_P_AU_WAT_D_14
BDW_060_100_05_N_P_AU_WAT_D_15
BDW_100_200_EV_N_P_AU_WAT_D_16
BDW_100_200_95_N_P_AU_WAT_D_17
BDW_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_109
2018-03-19T06:18:39Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_109
https://data.csiro.au/dap/
AWC_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
AWC_000_005_EV_N_P_AU_WAT_D_1
AWC_000_005_95_N_P_AU_WAT_D_2
AWC_000_005_05_N_P_AU_WAT_D_3
AWC_005_015_EV_N_P_AU_WAT_D_4
AWC_005_015_95_N_P_AU_WAT_D_5
AWC_005_015_05_N_P_AU_WAT_D_6
AWC_015_030_EV_N_P_AU_WAT_D_7
AWC_015_030_95_N_P_AU_WAT_D_8
AWC_015_030_05_N_P_AU_WAT_D_9
AWC_030_060_EV_N_P_AU_WAT_D_10
AWC_030_060_95_N_P_AU_WAT_D_11
AWC_030_060_05_N_P_AU_WAT_D_12
AWC_060_100_EV_N_P_AU_WAT_D_13
AWC_060_100_95_N_P_AU_WAT_D_14
AWC_060_100_05_N_P_AU_WAT_D_15
AWC_100_200_EV_N_P_AU_WAT_D_16
AWC_100_200_95_N_P_AU_WAT_D_17
AWC_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_110
2018-03-19T06:18:39Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_110
https://data.csiro.au/dap/
ECD_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/ECD_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
ECD_100_200_05_N_P_AU_WAT_D
layers
ECD_100_200_95_N_P_AU_WAT_D
layers
ECD_100_200_EV_N_P_AU_WAT_D
layers
ECD_060_100_05_N_P_AU_WAT_D
layers
ECD_060_100_95_N_P_AU_WAT_D
layers
ECD_060_100_EV_N_P_AU_WAT_D
layers
ECD_030_060_05_N_P_AU_WAT_D
layers
ECD_030_060_95_N_P_AU_WAT_D
layers
ECD_030_060_EV_N_P_AU_WAT_D
layers
ECD_015_030_05_N_P_AU_WAT_D
layers
ECD_015_030_95_N_P_AU_WAT_D
layers
ECD_015_030_EV_N_P_AU_WAT_D
layers
ECD_005_015_05_N_P_AU_WAT_D
layers
ECD_005_015_95_N_P_AU_WAT_D
layers
ECD_005_015_EV_N_P_AU_WAT_D
layers
ECD_000_005_05_N_P_AU_WAT_D
layers
ECD_000_005_95_N_P_AU_WAT_D
layers
ECD_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_111
2018-03-19T04:18:01Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_111
https://data.csiro.au/dap/
BDW_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
BDW_100_200_05_N_P_AU_TAS_N
layers
BDW_100_200_95_N_P_AU_TAS_N
layers
BDW_100_200_EV_N_P_AU_TAS_N
layers
BDW_060_100_05_N_P_AU_TAS_N
layers
BDW_060_100_95_N_P_AU_TAS_N
layers
BDW_060_100_EV_N_P_AU_TAS_N
layers
BDW_030_060_05_N_P_AU_TAS_N
layers
BDW_030_060_95_N_P_AU_TAS_N
layers
BDW_030_060_EV_N_P_AU_TAS_N
layers
BDW_015_030_05_N_P_AU_TAS_N
layers
BDW_015_030_95_N_P_AU_TAS_N
layers
BDW_015_030_EV_N_P_AU_TAS_N
layers
BDW_005_015_05_N_P_AU_TAS_N
layers
BDW_005_015_95_N_P_AU_TAS_N
layers
BDW_005_015_EV_N_P_AU_TAS_N
layers
BDW_000_005_05_N_P_AU_TAS_N
layers
BDW_000_005_95_N_P_AU_TAS_N
layers
BDW_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
Bulk Density (Whole Earth) is defined as the Bulk Density of the whole soil (including coarse fragments) in mass per unit volume. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_112
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_112
https://data.csiro.au/dap/
BDW_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
BDW_000_005_EV_N_P_AU_TAS_N_1
BDW_000_005_95_N_P_AU_TAS_N_2
BDW_000_005_05_N_P_AU_TAS_N_3
BDW_005_015_EV_N_P_AU_TAS_N_4
BDW_005_015_95_N_P_AU_TAS_N_5
BDW_005_015_05_N_P_AU_TAS_N_6
BDW_015_030_EV_N_P_AU_TAS_N_7
BDW_015_030_95_N_P_AU_TAS_N_8
BDW_015_030_05_N_P_AU_TAS_N_9
BDW_030_060_EV_N_P_AU_TAS_N_10
BDW_030_060_95_N_P_AU_TAS_N_11
BDW_030_060_05_N_P_AU_TAS_N_12
BDW_060_100_EV_N_P_AU_TAS_N_13
BDW_060_100_95_N_P_AU_TAS_N_14
BDW_060_100_05_N_P_AU_TAS_N_15
BDW_100_200_EV_N_P_AU_TAS_N_16
BDW_100_200_95_N_P_AU_TAS_N_17
BDW_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_113
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_113
https://data.csiro.au/dap/
CFG_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/CFG_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CFG_100_200_05_N_P_AU_TAS_N
layers
CFG_100_200_95_N_P_AU_TAS_N
layers
CFG_100_200_EV_N_P_AU_TAS_N
layers
CFG_060_100_05_N_P_AU_TAS_N
layers
CFG_060_100_95_N_P_AU_TAS_N
layers
CFG_060_100_EV_N_P_AU_TAS_N
layers
CFG_030_060_05_N_P_AU_TAS_N
layers
CFG_030_060_95_N_P_AU_TAS_N
layers
CFG_030_060_EV_N_P_AU_TAS_N
layers
CFG_015_030_05_N_P_AU_TAS_N
layers
CFG_015_030_95_N_P_AU_TAS_N
layers
CFG_015_030_EV_N_P_AU_TAS_N
layers
CFG_005_015_05_N_P_AU_TAS_N
layers
CFG_005_015_95_N_P_AU_TAS_N
layers
CFG_005_015_EV_N_P_AU_TAS_N
layers
CFG_000_005_05_N_P_AU_TAS_N
layers
CFG_000_005_95_N_P_AU_TAS_N
layers
CFG_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
Coarse Fragments is defined as the mass fraction of the soil material greater than 2 mm. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_124
2018-03-19T04:18:01Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_124
https://data.csiro.au/dap/
SND_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SND_000_005_EV_N_P_AU_TAS_N_1
SND_000_005_95_N_P_AU_TAS_N_2
SND_000_005_05_N_P_AU_TAS_N_3
SND_005_015_EV_N_P_AU_TAS_N_4
SND_005_015_95_N_P_AU_TAS_N_5
SND_005_015_05_N_P_AU_TAS_N_6
SND_015_030_EV_N_P_AU_TAS_N_7
SND_015_030_95_N_P_AU_TAS_N_8
SND_015_030_05_N_P_AU_TAS_N_9
SND_030_060_EV_N_P_AU_TAS_N_10
SND_030_060_95_N_P_AU_TAS_N_11
SND_030_060_05_N_P_AU_TAS_N_12
SND_060_100_EV_N_P_AU_TAS_N_13
SND_060_100_95_N_P_AU_TAS_N_14
SND_060_100_05_N_P_AU_TAS_N_15
SND_100_200_EV_N_P_AU_TAS_N_16
SND_100_200_95_N_P_AU_TAS_N_17
SND_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_125
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_125
https://data.csiro.au/dap/
SOC_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/SOC_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SOC_100_200_05_N_P_AU_TAS_N
layers
SOC_100_200_95_N_P_AU_TAS_N
layers
SOC_100_200_EV_N_P_AU_TAS_N
layers
SOC_060_100_05_N_P_AU_TAS_N
layers
SOC_060_100_95_N_P_AU_TAS_N
layers
SOC_060_100_EV_N_P_AU_TAS_N
layers
SOC_030_060_05_N_P_AU_TAS_N
layers
SOC_030_060_95_N_P_AU_TAS_N
layers
SOC_030_060_EV_N_P_AU_TAS_N
layers
SOC_015_030_05_N_P_AU_TAS_N
layers
SOC_015_030_95_N_P_AU_TAS_N
layers
SOC_015_030_EV_N_P_AU_TAS_N
layers
SOC_005_015_05_N_P_AU_TAS_N
layers
SOC_005_015_95_N_P_AU_TAS_N
layers
SOC_005_015_EV_N_P_AU_TAS_N
layers
SOC_000_005_05_N_P_AU_TAS_N
layers
SOC_000_005_95_N_P_AU_TAS_N
layers
SOC_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
Organic Carbon is defined as the mass fraction of carbon by weight in the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_126
2018-03-19T04:18:01Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_126
https://data.csiro.au/dap/
SOC_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/SOC_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SOC_000_005_EV_N_P_AU_TAS_N_1
SOC_000_005_95_N_P_AU_TAS_N_2
SOC_000_005_05_N_P_AU_TAS_N_3
SOC_005_015_EV_N_P_AU_TAS_N_4
SOC_005_015_95_N_P_AU_TAS_N_5
SOC_005_015_05_N_P_AU_TAS_N_6
SOC_015_030_EV_N_P_AU_TAS_N_7
SOC_015_030_95_N_P_AU_TAS_N_8
SOC_015_030_05_N_P_AU_TAS_N_9
SOC_030_060_EV_N_P_AU_TAS_N_10
SOC_030_060_95_N_P_AU_TAS_N_11
SOC_030_060_05_N_P_AU_TAS_N_12
SOC_060_100_EV_N_P_AU_TAS_N_13
SOC_060_100_95_N_P_AU_TAS_N_14
SOC_060_100_05_N_P_AU_TAS_N_15
SOC_100_200_EV_N_P_AU_TAS_N_16
SOC_100_200_95_N_P_AU_TAS_N_17
SOC_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_90
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_90
https://data.csiro.au/dap/
SND_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SND_000_005_EV_N_P_AU_WAT_D_1
SND_000_005_95_N_P_AU_WAT_D_2
SND_000_005_05_N_P_AU_WAT_D_3
SND_005_015_EV_N_P_AU_WAT_D_4
SND_005_015_95_N_P_AU_WAT_D_5
SND_005_015_05_N_P_AU_WAT_D_6
SND_015_030_EV_N_P_AU_WAT_D_7
SND_015_030_95_N_P_AU_WAT_D_8
SND_015_030_05_N_P_AU_WAT_D_9
SND_030_060_EV_N_P_AU_WAT_D_10
SND_030_060_95_N_P_AU_WAT_D_11
SND_030_060_05_N_P_AU_WAT_D_12
SND_060_100_EV_N_P_AU_WAT_D_13
SND_060_100_95_N_P_AU_WAT_D_14
SND_060_100_05_N_P_AU_WAT_D_15
SND_100_200_EV_N_P_AU_WAT_D_16
SND_100_200_95_N_P_AU_WAT_D_17
SND_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_91
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_91
https://data.csiro.au/dap/
SND_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SND_100_200_05_N_P_AU_WAT_D
layers
SND_100_200_95_N_P_AU_WAT_D
layers
SND_100_200_EV_N_P_AU_WAT_D
layers
SND_060_100_05_N_P_AU_WAT_D
layers
SND_060_100_95_N_P_AU_WAT_D
layers
SND_060_100_EV_N_P_AU_WAT_D
layers
SND_030_060_05_N_P_AU_WAT_D
layers
SND_030_060_95_N_P_AU_WAT_D
layers
SND_030_060_EV_N_P_AU_WAT_D
layers
SND_015_030_05_N_P_AU_WAT_D
layers
SND_015_030_95_N_P_AU_WAT_D
layers
SND_015_030_EV_N_P_AU_WAT_D
layers
SND_005_015_05_N_P_AU_WAT_D
layers
SND_005_015_95_N_P_AU_WAT_D
layers
SND_005_015_EV_N_P_AU_WAT_D
layers
SND_000_005_05_N_P_AU_WAT_D
layers
SND_000_005_95_N_P_AU_WAT_D
layers
SND_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
Sand content is defined as the 20 um - 2 mm mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_93
2018-03-19T06:18:39Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_93
https://data.csiro.au/dap/
SLT_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SLT_100_200_05_N_P_AU_WAT_D
layers
SLT_100_200_95_N_P_AU_WAT_D
layers
SLT_100_200_EV_N_P_AU_WAT_D
layers
SLT_060_100_05_N_P_AU_WAT_D
layers
SLT_060_100_95_N_P_AU_WAT_D
layers
SLT_060_100_EV_N_P_AU_WAT_D
layers
SLT_030_060_05_N_P_AU_WAT_D
layers
SLT_030_060_95_N_P_AU_WAT_D
layers
SLT_030_060_EV_N_P_AU_WAT_D
layers
SLT_015_030_05_N_P_AU_WAT_D
layers
SLT_015_030_95_N_P_AU_WAT_D
layers
SLT_015_030_EV_N_P_AU_WAT_D
layers
SLT_005_015_05_N_P_AU_WAT_D
layers
SLT_005_015_95_N_P_AU_WAT_D
layers
SLT_005_015_EV_N_P_AU_WAT_D
layers
SLT_000_005_05_N_P_AU_WAT_D
layers
SLT_000_005_95_N_P_AU_WAT_D
layers
SLT_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
Silt content is defined as the 2 - 20 um mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_96
2018-03-19T06:18:39Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_96
https://data.csiro.au/dap/
SLT_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SLT_000_005_EV_N_P_AU_WAT_D_1
SLT_000_005_95_N_P_AU_WAT_D_2
SLT_000_005_05_N_P_AU_WAT_D_3
SLT_005_015_EV_N_P_AU_WAT_D_4
SLT_005_015_95_N_P_AU_WAT_D_5
SLT_005_015_05_N_P_AU_WAT_D_6
SLT_015_030_EV_N_P_AU_WAT_D_7
SLT_015_030_95_N_P_AU_WAT_D_8
SLT_015_030_05_N_P_AU_WAT_D_9
SLT_030_060_EV_N_P_AU_WAT_D_10
SLT_030_060_95_N_P_AU_WAT_D_11
SLT_030_060_05_N_P_AU_WAT_D_12
SLT_060_100_EV_N_P_AU_WAT_D_13
SLT_060_100_95_N_P_AU_WAT_D_14
SLT_060_100_05_N_P_AU_WAT_D_15
SLT_100_200_EV_N_P_AU_WAT_D_16
SLT_100_200_95_N_P_AU_WAT_D_17
SLT_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_97
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_97
https://data.csiro.au/dap/
PHW_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/PHW_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
PHW_000_005_EV_N_P_AU_WAT_D_1
PHW_000_005_95_N_P_AU_WAT_D_2
PHW_000_005_05_N_P_AU_WAT_D_3
PHW_005_015_EV_N_P_AU_WAT_D_4
PHW_005_015_95_N_P_AU_WAT_D_5
PHW_005_015_05_N_P_AU_WAT_D_6
PHW_015_030_EV_N_P_AU_WAT_D_7
PHW_015_030_95_N_P_AU_WAT_D_8
PHW_015_030_05_N_P_AU_WAT_D_9
PHW_030_060_EV_N_P_AU_WAT_D_10
PHW_030_060_95_N_P_AU_WAT_D_11
PHW_030_060_05_N_P_AU_WAT_D_12
PHW_060_100_EV_N_P_AU_WAT_D_13
PHW_060_100_95_N_P_AU_WAT_D_14
PHW_060_100_05_N_P_AU_WAT_D_15
PHW_100_200_EV_N_P_AU_WAT_D_16
PHW_100_200_95_N_P_AU_WAT_D_17
PHW_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_98
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_98
https://data.csiro.au/dap/
PHW_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/PHW_ACLEP_AU_WAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
PHW_100_200_05_N_P_AU_WAT_D
layers
PHW_100_200_95_N_P_AU_WAT_D
layers
PHW_100_200_EV_N_P_AU_WAT_D
layers
PHW_060_100_05_N_P_AU_WAT_D
layers
PHW_060_100_95_N_P_AU_WAT_D
layers
PHW_060_100_EV_N_P_AU_WAT_D
layers
PHW_030_060_05_N_P_AU_WAT_D
layers
PHW_030_060_95_N_P_AU_WAT_D
layers
PHW_030_060_EV_N_P_AU_WAT_D
layers
PHW_015_030_05_N_P_AU_WAT_D
layers
PHW_015_030_95_N_P_AU_WAT_D
layers
PHW_015_030_EV_N_P_AU_WAT_D
layers
PHW_005_015_05_N_P_AU_WAT_D
layers
PHW_005_015_95_N_P_AU_WAT_D
layers
PHW_005_015_EV_N_P_AU_WAT_D
layers
PHW_000_005_05_N_P_AU_WAT_D
layers
PHW_000_005_95_N_P_AU_WAT_D
layers
PHW_000_005_EV_N_P_AU_WAT_D
layers
northlimit=-13.742917; southlimit=-35.134583; westlimit=112.999583; eastLimit=129.095417; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
pH in Water is defined as the pH of a 1:5 soil/water solution. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_99
2018-03-19T06:18:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_99
https://data.csiro.au/dap/
CFG_ACLEP_AU_WAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CFG_ACLEP_AU_WAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CFG_000_005_EV_N_P_AU_WAT_D_1
CFG_000_005_95_N_P_AU_WAT_D_2
CFG_000_005_05_N_P_AU_WAT_D_3
CFG_005_015_EV_N_P_AU_WAT_D_4
CFG_005_015_95_N_P_AU_WAT_D_5
CFG_005_015_05_N_P_AU_WAT_D_6
CFG_015_030_EV_N_P_AU_WAT_D_7
CFG_015_030_95_N_P_AU_WAT_D_8
CFG_015_030_05_N_P_AU_WAT_D_9
CFG_030_060_EV_N_P_AU_WAT_D_10
CFG_030_060_95_N_P_AU_WAT_D_11
CFG_030_060_05_N_P_AU_WAT_D_12
CFG_060_100_EV_N_P_AU_WAT_D_13
CFG_060_100_95_N_P_AU_WAT_D_14
CFG_060_100_05_N_P_AU_WAT_D_15
CFG_100_200_EV_N_P_AU_WAT_D_16
CFG_100_200_95_N_P_AU_WAT_D_17
CFG_100_200_05_N_P_AU_WAT_D_18
northlimit=-13.742916669435452; southlimit=-35.13458333567033; westlimit=112.99958333299942; eastLimit=129.09541666600785; projection=WGS84
csiro:02bf5c7e-b901-4a09-844a-53a9e011bb66
102.100.100/16122
csiro:service_114
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_114
https://data.csiro.au/dap/
CFG_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/CFG_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CFG_000_005_EV_N_P_AU_TAS_N_1
CFG_000_005_95_N_P_AU_TAS_N_2
CFG_000_005_05_N_P_AU_TAS_N_3
CFG_005_015_EV_N_P_AU_TAS_N_4
CFG_005_015_95_N_P_AU_TAS_N_5
CFG_005_015_05_N_P_AU_TAS_N_6
CFG_015_030_EV_N_P_AU_TAS_N_7
CFG_015_030_95_N_P_AU_TAS_N_8
CFG_015_030_05_N_P_AU_TAS_N_9
CFG_030_060_EV_N_P_AU_TAS_N_10
CFG_030_060_95_N_P_AU_TAS_N_11
CFG_030_060_05_N_P_AU_TAS_N_12
CFG_060_100_EV_N_P_AU_TAS_N_13
CFG_060_100_95_N_P_AU_TAS_N_14
CFG_060_100_05_N_P_AU_TAS_N_15
CFG_100_200_EV_N_P_AU_TAS_N_16
CFG_100_200_95_N_P_AU_TAS_N_17
CFG_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_115
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_115
https://data.csiro.au/dap/
CLY_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CLY_100_200_05_N_P_AU_TAS_N
layers
CLY_100_200_95_N_P_AU_TAS_N
layers
CLY_100_200_EV_N_P_AU_TAS_N
layers
CLY_060_100_05_N_P_AU_TAS_N
layers
CLY_060_100_95_N_P_AU_TAS_N
layers
CLY_060_100_EV_N_P_AU_TAS_N
layers
CLY_030_060_05_N_P_AU_TAS_N
layers
CLY_030_060_95_N_P_AU_TAS_N
layers
CLY_030_060_EV_N_P_AU_TAS_N
layers
CLY_015_030_05_N_P_AU_TAS_N
layers
CLY_015_030_95_N_P_AU_TAS_N
layers
CLY_015_030_EV_N_P_AU_TAS_N
layers
CLY_005_015_05_N_P_AU_TAS_N
layers
CLY_005_015_95_N_P_AU_TAS_N
layers
CLY_005_015_EV_N_P_AU_TAS_N
layers
CLY_000_005_05_N_P_AU_TAS_N
layers
CLY_000_005_95_N_P_AU_TAS_N
layers
CLY_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
WMS
csiro:service_116
2018-03-19T04:18:01Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_116
https://data.csiro.au/dap/
CLY_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CLY_000_005_EV_N_P_AU_TAS_N_1
CLY_000_005_95_N_P_AU_TAS_N_2
CLY_000_005_05_N_P_AU_TAS_N_3
CLY_005_015_EV_N_P_AU_TAS_N_4
CLY_005_015_95_N_P_AU_TAS_N_5
CLY_005_015_05_N_P_AU_TAS_N_6
CLY_015_030_EV_N_P_AU_TAS_N_7
CLY_015_030_95_N_P_AU_TAS_N_8
CLY_015_030_05_N_P_AU_TAS_N_9
CLY_030_060_EV_N_P_AU_TAS_N_10
CLY_030_060_95_N_P_AU_TAS_N_11
CLY_030_060_05_N_P_AU_TAS_N_12
CLY_060_100_EV_N_P_AU_TAS_N_13
CLY_060_100_95_N_P_AU_TAS_N_14
CLY_060_100_05_N_P_AU_TAS_N_15
CLY_100_200_EV_N_P_AU_TAS_N_16
CLY_100_200_95_N_P_AU_TAS_N_17
CLY_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_117
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_117
https://data.csiro.au/dap/
ECD_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/ECD_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
ECD_100_200_05_N_P_AU_TAS_N
layers
ECD_100_200_95_N_P_AU_TAS_N
layers
ECD_100_200_EV_N_P_AU_TAS_N
layers
ECD_060_100_05_N_P_AU_TAS_N
layers
ECD_060_100_95_N_P_AU_TAS_N
layers
ECD_060_100_EV_N_P_AU_TAS_N
layers
ECD_030_060_05_N_P_AU_TAS_N
layers
ECD_030_060_95_N_P_AU_TAS_N
layers
ECD_030_060_EV_N_P_AU_TAS_N
layers
ECD_015_030_05_N_P_AU_TAS_N
layers
ECD_015_030_95_N_P_AU_TAS_N
layers
ECD_015_030_EV_N_P_AU_TAS_N
layers
ECD_005_015_05_N_P_AU_TAS_N
layers
ECD_005_015_95_N_P_AU_TAS_N
layers
ECD_005_015_EV_N_P_AU_TAS_N
layers
ECD_000_005_05_N_P_AU_TAS_N
layers
ECD_000_005_95_N_P_AU_TAS_N
layers
ECD_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
Electrical Conductivity is defined as the Conductivity of a 1:5 soil/water solution. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_118
2018-03-19T04:18:01Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_118
https://data.csiro.au/dap/
ECD_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/ECD_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
ECD_000_005_EV_N_P_AU_TAS_N_1
ECD_000_005_95_N_P_AU_TAS_N_2
ECD_000_005_05_N_P_AU_TAS_N_3
ECD_005_015_EV_N_P_AU_TAS_N_4
ECD_005_015_95_N_P_AU_TAS_N_5
ECD_005_015_05_N_P_AU_TAS_N_6
ECD_015_030_EV_N_P_AU_TAS_N_7
ECD_015_030_95_N_P_AU_TAS_N_8
ECD_015_030_05_N_P_AU_TAS_N_9
ECD_030_060_EV_N_P_AU_TAS_N_10
ECD_030_060_95_N_P_AU_TAS_N_11
ECD_030_060_05_N_P_AU_TAS_N_12
ECD_060_100_EV_N_P_AU_TAS_N_13
ECD_060_100_95_N_P_AU_TAS_N_14
ECD_060_100_05_N_P_AU_TAS_N_15
ECD_100_200_EV_N_P_AU_TAS_N_16
ECD_100_200_95_N_P_AU_TAS_N_17
ECD_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_119
2018-03-19T04:18:01Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_119
https://data.csiro.au/dap/
PHW_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/PHW_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
PHW_100_200_05_N_P_AU_TAS_N
layers
PHW_100_200_95_N_P_AU_TAS_N
layers
PHW_100_200_EV_N_P_AU_TAS_N
layers
PHW_060_100_05_N_P_AU_TAS_N
layers
PHW_060_100_95_N_P_AU_TAS_N
layers
PHW_060_100_EV_N_P_AU_TAS_N
layers
PHW_030_060_05_N_P_AU_TAS_N
layers
PHW_030_060_95_N_P_AU_TAS_N
layers
PHW_030_060_EV_N_P_AU_TAS_N
layers
PHW_015_030_05_N_P_AU_TAS_N
layers
PHW_015_030_95_N_P_AU_TAS_N
layers
PHW_015_030_EV_N_P_AU_TAS_N
layers
PHW_005_015_05_N_P_AU_TAS_N
layers
PHW_005_015_95_N_P_AU_TAS_N
layers
PHW_005_015_EV_N_P_AU_TAS_N
layers
PHW_000_005_05_N_P_AU_TAS_N
layers
PHW_000_005_95_N_P_AU_TAS_N
layers
PHW_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
pH in Water is defined as the pH of a 1:5 soil/water solution. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_120
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_120
https://data.csiro.au/dap/
PHW_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/PHW_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
PHW_000_005_EV_N_P_AU_TAS_N_1
PHW_000_005_95_N_P_AU_TAS_N_2
PHW_000_005_05_N_P_AU_TAS_N_3
PHW_005_015_EV_N_P_AU_TAS_N_4
PHW_005_015_95_N_P_AU_TAS_N_5
PHW_005_015_05_N_P_AU_TAS_N_6
PHW_015_030_EV_N_P_AU_TAS_N_7
PHW_015_030_95_N_P_AU_TAS_N_8
PHW_015_030_05_N_P_AU_TAS_N_9
PHW_030_060_EV_N_P_AU_TAS_N_10
PHW_030_060_95_N_P_AU_TAS_N_11
PHW_030_060_05_N_P_AU_TAS_N_12
PHW_060_100_EV_N_P_AU_TAS_N_13
PHW_060_100_95_N_P_AU_TAS_N_14
PHW_060_100_05_N_P_AU_TAS_N_15
PHW_100_200_EV_N_P_AU_TAS_N_16
PHW_100_200_95_N_P_AU_TAS_N_17
PHW_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_121
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_121
https://data.csiro.au/dap/
SLT_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SLT_100_200_05_N_P_AU_TAS_N
layers
SLT_100_200_95_N_P_AU_TAS_N
layers
SLT_100_200_EV_N_P_AU_TAS_N
layers
SLT_060_100_05_N_P_AU_TAS_N
layers
SLT_060_100_95_N_P_AU_TAS_N
layers
SLT_060_100_EV_N_P_AU_TAS_N
layers
SLT_030_060_05_N_P_AU_TAS_N
layers
SLT_030_060_95_N_P_AU_TAS_N
layers
SLT_030_060_EV_N_P_AU_TAS_N
layers
SLT_015_030_05_N_P_AU_TAS_N
layers
SLT_015_030_95_N_P_AU_TAS_N
layers
SLT_015_030_EV_N_P_AU_TAS_N
layers
SLT_005_015_05_N_P_AU_TAS_N
layers
SLT_005_015_95_N_P_AU_TAS_N
layers
SLT_005_015_EV_N_P_AU_TAS_N
layers
SLT_000_005_05_N_P_AU_TAS_N
layers
SLT_000_005_95_N_P_AU_TAS_N
layers
SLT_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
Silt content is defined as the 2 - 20 um mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_122
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_122
https://data.csiro.au/dap/
SLT_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_TAS_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SLT_000_005_EV_N_P_AU_TAS_N_1
SLT_000_005_95_N_P_AU_TAS_N_2
SLT_000_005_05_N_P_AU_TAS_N_3
SLT_005_015_EV_N_P_AU_TAS_N_4
SLT_005_015_95_N_P_AU_TAS_N_5
SLT_005_015_05_N_P_AU_TAS_N_6
SLT_015_030_EV_N_P_AU_TAS_N_7
SLT_015_030_95_N_P_AU_TAS_N_8
SLT_015_030_05_N_P_AU_TAS_N_9
SLT_030_060_EV_N_P_AU_TAS_N_10
SLT_030_060_95_N_P_AU_TAS_N_11
SLT_030_060_05_N_P_AU_TAS_N_12
SLT_060_100_EV_N_P_AU_TAS_N_13
SLT_060_100_95_N_P_AU_TAS_N_14
SLT_060_100_05_N_P_AU_TAS_N_15
SLT_100_200_EV_N_P_AU_TAS_N_16
SLT_100_200_95_N_P_AU_TAS_N_17
SLT_100_200_05_N_P_AU_TAS_N_18
northlimit=-39.3779166669397; southlimit=-43.7062500003428; westlimit=143.734583333896; eastLimit=148.650416667308; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
csiro:service_123
2018-03-19T04:18:01Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_123
https://data.csiro.au/dap/
SND_ACLEP_AU_TAS_N
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_TAS_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SND_100_200_05_N_P_AU_TAS_N
layers
SND_100_200_95_N_P_AU_TAS_N
layers
SND_100_200_EV_N_P_AU_TAS_N
layers
SND_060_100_05_N_P_AU_TAS_N
layers
SND_060_100_95_N_P_AU_TAS_N
layers
SND_060_100_EV_N_P_AU_TAS_N
layers
SND_030_060_05_N_P_AU_TAS_N
layers
SND_030_060_95_N_P_AU_TAS_N
layers
SND_030_060_EV_N_P_AU_TAS_N
layers
SND_015_030_05_N_P_AU_TAS_N
layers
SND_015_030_95_N_P_AU_TAS_N
layers
SND_015_030_EV_N_P_AU_TAS_N
layers
SND_005_015_05_N_P_AU_TAS_N
layers
SND_005_015_95_N_P_AU_TAS_N
layers
SND_005_015_EV_N_P_AU_TAS_N
layers
SND_000_005_05_N_P_AU_TAS_N
layers
SND_000_005_95_N_P_AU_TAS_N
layers
SND_000_005_EV_N_P_AU_TAS_N
layers
northlimit=-39.377917; southlimit=-43.70625; westlimit=143.734583; eastLimit=148.650417; projection=WGS84
csiro:48164382-5df9-483c-a22b-068112b92365
102.100.100/16128
Sand content is defined as the 20 um - 2 mm mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_128
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_128
https://data.csiro.au/dap/
SND_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SND_000_005_EV_N_P_AU_SAT_D_1
SND_000_005_95_N_P_AU_SAT_D_2
SND_000_005_05_N_P_AU_SAT_D_3
SND_005_015_EV_N_P_AU_SAT_D_4
SND_005_015_95_N_P_AU_SAT_D_5
SND_005_015_05_N_P_AU_SAT_D_6
SND_015_030_EV_N_P_AU_SAT_D_7
SND_015_030_95_N_P_AU_SAT_D_8
SND_015_030_05_N_P_AU_SAT_D_9
SND_030_060_EV_N_P_AU_SAT_D_10
SND_030_060_95_N_P_AU_SAT_D_11
SND_030_060_05_N_P_AU_SAT_D_12
SND_060_100_EV_N_P_AU_SAT_D_13
SND_060_100_95_N_P_AU_SAT_D_14
SND_060_100_05_N_P_AU_SAT_D_15
SND_100_200_EV_N_P_AU_SAT_D_16
SND_100_200_95_N_P_AU_SAT_D_17
SND_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_129
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_129
https://data.csiro.au/dap/
BDW_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
BDW_000_005_EV_N_P_AU_SAT_D_1
BDW_000_005_95_N_P_AU_SAT_D_2
BDW_000_005_05_N_P_AU_SAT_D_3
BDW_005_015_EV_N_P_AU_SAT_D_4
BDW_005_015_95_N_P_AU_SAT_D_5
BDW_005_015_05_N_P_AU_SAT_D_6
BDW_015_030_EV_N_P_AU_SAT_D_7
BDW_015_030_95_N_P_AU_SAT_D_8
BDW_015_030_05_N_P_AU_SAT_D_9
BDW_030_060_EV_N_P_AU_SAT_D_10
BDW_030_060_95_N_P_AU_SAT_D_11
BDW_030_060_05_N_P_AU_SAT_D_12
BDW_060_100_EV_N_P_AU_SAT_D_13
BDW_060_100_95_N_P_AU_SAT_D_14
BDW_060_100_05_N_P_AU_SAT_D_15
BDW_100_200_EV_N_P_AU_SAT_D_16
BDW_100_200_95_N_P_AU_SAT_D_17
BDW_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_130
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_130
https://data.csiro.au/dap/
PHC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/PHC_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
PHC_000_005_EV_N_P_AU_SAT_D_1
PHC_000_005_95_N_P_AU_SAT_D_2
PHC_000_005_05_N_P_AU_SAT_D_3
PHC_005_015_EV_N_P_AU_SAT_D_4
PHC_005_015_95_N_P_AU_SAT_D_5
PHC_005_015_05_N_P_AU_SAT_D_6
PHC_015_030_EV_N_P_AU_SAT_D_7
PHC_015_030_95_N_P_AU_SAT_D_8
PHC_015_030_05_N_P_AU_SAT_D_9
PHC_030_060_EV_N_P_AU_SAT_D_10
PHC_030_060_95_N_P_AU_SAT_D_11
PHC_030_060_05_N_P_AU_SAT_D_12
PHC_060_100_EV_N_P_AU_SAT_D_13
PHC_060_100_95_N_P_AU_SAT_D_14
PHC_060_100_05_N_P_AU_SAT_D_15
PHC_100_200_EV_N_P_AU_SAT_D_16
PHC_100_200_95_N_P_AU_SAT_D_17
PHC_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_131
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_131
https://data.csiro.au/dap/
CEC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CEC_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CEC_100_200_05_N_P_AU_SAT_D
layers
CEC_100_200_95_N_P_AU_SAT_D
layers
CEC_100_200_EV_N_P_AU_SAT_D
layers
CEC_060_100_05_N_P_AU_SAT_D
layers
CEC_060_100_95_N_P_AU_SAT_D
layers
CEC_060_100_EV_N_P_AU_SAT_D
layers
CEC_030_060_05_N_P_AU_SAT_D
layers
CEC_030_060_95_N_P_AU_SAT_D
layers
CEC_030_060_EV_N_P_AU_SAT_D
layers
CEC_015_030_05_N_P_AU_SAT_D
layers
CEC_015_030_95_N_P_AU_SAT_D
layers
CEC_015_030_EV_N_P_AU_SAT_D
layers
CEC_005_015_05_N_P_AU_SAT_D
layers
CEC_005_015_95_N_P_AU_SAT_D
layers
CEC_005_015_EV_N_P_AU_SAT_D
layers
CEC_000_005_05_N_P_AU_SAT_D
layers
CEC_000_005_95_N_P_AU_SAT_D
layers
CEC_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
Cation Exchange Capacity is defined as the concentration of cations extracted using barium chloride (BaCl2). A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_132
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_132
https://data.csiro.au/dap/
SLT_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SLT_100_200_05_N_P_AU_SAT_D
layers
SLT_100_200_95_N_P_AU_SAT_D
layers
SLT_100_200_EV_N_P_AU_SAT_D
layers
SLT_060_100_05_N_P_AU_SAT_D
layers
SLT_060_100_95_N_P_AU_SAT_D
layers
SLT_060_100_EV_N_P_AU_SAT_D
layers
SLT_030_060_05_N_P_AU_SAT_D
layers
SLT_030_060_95_N_P_AU_SAT_D
layers
SLT_030_060_EV_N_P_AU_SAT_D
layers
SLT_015_030_05_N_P_AU_SAT_D
layers
SLT_015_030_95_N_P_AU_SAT_D
layers
SLT_015_030_EV_N_P_AU_SAT_D
layers
SLT_005_015_05_N_P_AU_SAT_D
layers
SLT_005_015_95_N_P_AU_SAT_D
layers
SLT_005_015_EV_N_P_AU_SAT_D
layers
SLT_000_005_05_N_P_AU_SAT_D
layers
SLT_000_005_95_N_P_AU_SAT_D
layers
SLT_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
Silt content is defined as the 2 - 20 um mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_133
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_133
https://data.csiro.au/dap/
SLT_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SLT_000_005_EV_N_P_AU_SAT_D_1
SLT_000_005_95_N_P_AU_SAT_D_2
SLT_000_005_05_N_P_AU_SAT_D_3
SLT_005_015_EV_N_P_AU_SAT_D_4
SLT_005_015_95_N_P_AU_SAT_D_5
SLT_005_015_05_N_P_AU_SAT_D_6
SLT_015_030_EV_N_P_AU_SAT_D_7
SLT_015_030_95_N_P_AU_SAT_D_8
SLT_015_030_05_N_P_AU_SAT_D_9
SLT_030_060_EV_N_P_AU_SAT_D_10
SLT_030_060_95_N_P_AU_SAT_D_11
SLT_030_060_05_N_P_AU_SAT_D_12
SLT_060_100_EV_N_P_AU_SAT_D_13
SLT_060_100_95_N_P_AU_SAT_D_14
SLT_060_100_05_N_P_AU_SAT_D_15
SLT_100_200_EV_N_P_AU_SAT_D_16
SLT_100_200_95_N_P_AU_SAT_D_17
SLT_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_134
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_134
https://data.csiro.au/dap/
BDW_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
BDW_100_200_05_N_P_AU_SAT_D
layers
BDW_100_200_95_N_P_AU_SAT_D
layers
BDW_100_200_EV_N_P_AU_SAT_D
layers
BDW_060_100_05_N_P_AU_SAT_D
layers
BDW_060_100_95_N_P_AU_SAT_D
layers
BDW_060_100_EV_N_P_AU_SAT_D
layers
BDW_030_060_05_N_P_AU_SAT_D
layers
BDW_030_060_95_N_P_AU_SAT_D
layers
BDW_030_060_EV_N_P_AU_SAT_D
layers
BDW_015_030_05_N_P_AU_SAT_D
layers
BDW_015_030_95_N_P_AU_SAT_D
layers
BDW_015_030_EV_N_P_AU_SAT_D
layers
BDW_005_015_05_N_P_AU_SAT_D
layers
BDW_005_015_95_N_P_AU_SAT_D
layers
BDW_005_015_EV_N_P_AU_SAT_D
layers
BDW_000_005_05_N_P_AU_SAT_D
layers
BDW_000_005_95_N_P_AU_SAT_D
layers
BDW_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
Bulk Density (Whole Earth) is defined as the Bulk Density of the whole soil (including coarse fragments) in mass per unit volume. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_135
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_135
https://data.csiro.au/dap/
CLY_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CLY_100_200_05_N_P_AU_SAT_D
layers
CLY_100_200_95_N_P_AU_SAT_D
layers
CLY_100_200_EV_N_P_AU_SAT_D
layers
CLY_060_100_05_N_P_AU_SAT_D
layers
CLY_060_100_95_N_P_AU_SAT_D
layers
CLY_060_100_EV_N_P_AU_SAT_D
layers
CLY_030_060_05_N_P_AU_SAT_D
layers
CLY_030_060_95_N_P_AU_SAT_D
layers
CLY_030_060_EV_N_P_AU_SAT_D
layers
CLY_015_030_05_N_P_AU_SAT_D
layers
CLY_015_030_95_N_P_AU_SAT_D
layers
CLY_015_030_EV_N_P_AU_SAT_D
layers
CLY_005_015_05_N_P_AU_SAT_D
layers
CLY_005_015_95_N_P_AU_SAT_D
layers
CLY_005_015_EV_N_P_AU_SAT_D
layers
CLY_000_005_05_N_P_AU_SAT_D
layers
CLY_000_005_95_N_P_AU_SAT_D
layers
CLY_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_136
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_136
https://data.csiro.au/dap/
ECD_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/ECD_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
ECD_000_005_EV_N_P_AU_SAT_D_1
ECD_000_005_95_N_P_AU_SAT_D_2
ECD_000_005_05_N_P_AU_SAT_D_3
ECD_005_015_EV_N_P_AU_SAT_D_4
ECD_005_015_95_N_P_AU_SAT_D_5
ECD_005_015_05_N_P_AU_SAT_D_6
ECD_015_030_EV_N_P_AU_SAT_D_7
ECD_015_030_95_N_P_AU_SAT_D_8
ECD_015_030_05_N_P_AU_SAT_D_9
ECD_030_060_EV_N_P_AU_SAT_D_10
ECD_030_060_95_N_P_AU_SAT_D_11
ECD_030_060_05_N_P_AU_SAT_D_12
ECD_060_100_EV_N_P_AU_SAT_D_13
ECD_060_100_95_N_P_AU_SAT_D_14
ECD_060_100_05_N_P_AU_SAT_D_15
ECD_100_200_EV_N_P_AU_SAT_D_16
ECD_100_200_95_N_P_AU_SAT_D_17
ECD_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_137
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_137
https://data.csiro.au/dap/
AWC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
AWC_100_200_05_N_P_AU_SAT_D
layers
AWC_100_200_95_N_P_AU_SAT_D
layers
AWC_100_200_EV_N_P_AU_SAT_D
layers
AWC_060_100_05_N_P_AU_SAT_D
layers
AWC_060_100_95_N_P_AU_SAT_D
layers
AWC_060_100_EV_N_P_AU_SAT_D
layers
AWC_030_060_05_N_P_AU_SAT_D
layers
AWC_030_060_95_N_P_AU_SAT_D
layers
AWC_030_060_EV_N_P_AU_SAT_D
layers
AWC_015_030_05_N_P_AU_SAT_D
layers
AWC_015_030_95_N_P_AU_SAT_D
layers
AWC_015_030_EV_N_P_AU_SAT_D
layers
AWC_005_015_05_N_P_AU_SAT_D
layers
AWC_005_015_95_N_P_AU_SAT_D
layers
AWC_005_015_EV_N_P_AU_SAT_D
layers
AWC_000_005_05_N_P_AU_SAT_D
layers
AWC_000_005_95_N_P_AU_SAT_D
layers
AWC_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
Available Water Capacity is defined as the available water capacity computed for each of the specified depth increments. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_138
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_138
https://data.csiro.au/dap/
CFG_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CFG_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CFG_100_200_05_N_P_AU_SAT_D
layers
CFG_100_200_95_N_P_AU_SAT_D
layers
CFG_100_200_EV_N_P_AU_SAT_D
layers
CFG_060_100_05_N_P_AU_SAT_D
layers
CFG_060_100_95_N_P_AU_SAT_D
layers
CFG_060_100_EV_N_P_AU_SAT_D
layers
CFG_030_060_05_N_P_AU_SAT_D
layers
CFG_030_060_95_N_P_AU_SAT_D
layers
CFG_030_060_EV_N_P_AU_SAT_D
layers
CFG_015_030_05_N_P_AU_SAT_D
layers
CFG_015_030_95_N_P_AU_SAT_D
layers
CFG_015_030_EV_N_P_AU_SAT_D
layers
CFG_005_015_05_N_P_AU_SAT_D
layers
CFG_005_015_95_N_P_AU_SAT_D
layers
CFG_005_015_EV_N_P_AU_SAT_D
layers
CFG_000_005_05_N_P_AU_SAT_D
layers
CFG_000_005_95_N_P_AU_SAT_D
layers
CFG_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
Coarse Fragments is defined as the mass fraction of the soil material greater than 2 mm. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_139
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_139
https://data.csiro.au/dap/
CEC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CEC_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CEC_000_005_EV_N_P_AU_SAT_D_1
CEC_000_005_95_N_P_AU_SAT_D_2
CEC_000_005_05_N_P_AU_SAT_D_3
CEC_005_015_EV_N_P_AU_SAT_D_4
CEC_005_015_95_N_P_AU_SAT_D_5
CEC_005_015_05_N_P_AU_SAT_D_6
CEC_015_030_EV_N_P_AU_SAT_D_7
CEC_015_030_95_N_P_AU_SAT_D_8
CEC_015_030_05_N_P_AU_SAT_D_9
CEC_030_060_EV_N_P_AU_SAT_D_10
CEC_030_060_95_N_P_AU_SAT_D_11
CEC_030_060_05_N_P_AU_SAT_D_12
CEC_060_100_EV_N_P_AU_SAT_D_13
CEC_060_100_95_N_P_AU_SAT_D_14
CEC_060_100_05_N_P_AU_SAT_D_15
CEC_100_200_EV_N_P_AU_SAT_D_16
CEC_100_200_95_N_P_AU_SAT_D_17
CEC_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_140
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_140
https://data.csiro.au/dap/
SOC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SOC_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SOC_000_005_EV_N_P_AU_SAT_D_1
SOC_000_005_95_N_P_AU_SAT_D_2
SOC_000_005_05_N_P_AU_SAT_D_3
SOC_005_015_EV_N_P_AU_SAT_D_4
SOC_005_015_95_N_P_AU_SAT_D_5
SOC_005_015_05_N_P_AU_SAT_D_6
SOC_015_030_EV_N_P_AU_SAT_D_7
SOC_015_030_95_N_P_AU_SAT_D_8
SOC_015_030_05_N_P_AU_SAT_D_9
SOC_030_060_EV_N_P_AU_SAT_D_10
SOC_030_060_95_N_P_AU_SAT_D_11
SOC_030_060_05_N_P_AU_SAT_D_12
SOC_060_100_EV_N_P_AU_SAT_D_13
SOC_060_100_95_N_P_AU_SAT_D_14
SOC_060_100_05_N_P_AU_SAT_D_15
SOC_100_200_EV_N_P_AU_SAT_D_16
SOC_100_200_95_N_P_AU_SAT_D_17
SOC_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_141
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_141
https://data.csiro.au/dap/
SOC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SOC_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SOC_100_200_05_N_P_AU_SAT_D
layers
SOC_100_200_95_N_P_AU_SAT_D
layers
SOC_100_200_EV_N_P_AU_SAT_D
layers
SOC_060_100_05_N_P_AU_SAT_D
layers
SOC_060_100_95_N_P_AU_SAT_D
layers
SOC_060_100_EV_N_P_AU_SAT_D
layers
SOC_030_060_05_N_P_AU_SAT_D
layers
SOC_030_060_95_N_P_AU_SAT_D
layers
SOC_030_060_EV_N_P_AU_SAT_D
layers
SOC_015_030_05_N_P_AU_SAT_D
layers
SOC_015_030_95_N_P_AU_SAT_D
layers
SOC_015_030_EV_N_P_AU_SAT_D
layers
SOC_005_015_05_N_P_AU_SAT_D
layers
SOC_005_015_95_N_P_AU_SAT_D
layers
SOC_005_015_EV_N_P_AU_SAT_D
layers
SOC_000_005_05_N_P_AU_SAT_D
layers
SOC_000_005_95_N_P_AU_SAT_D
layers
SOC_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
Organic Carbon is defined as the mass fraction of carbon by weight in the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_142
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_142
https://data.csiro.au/dap/
CLY_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CLY_000_005_EV_N_P_AU_SAT_D_1
CLY_000_005_95_N_P_AU_SAT_D_2
CLY_000_005_05_N_P_AU_SAT_D_3
CLY_005_015_EV_N_P_AU_SAT_D_4
CLY_005_015_95_N_P_AU_SAT_D_5
CLY_005_015_05_N_P_AU_SAT_D_6
CLY_015_030_EV_N_P_AU_SAT_D_7
CLY_015_030_95_N_P_AU_SAT_D_8
CLY_015_030_05_N_P_AU_SAT_D_9
CLY_030_060_EV_N_P_AU_SAT_D_10
CLY_030_060_95_N_P_AU_SAT_D_11
CLY_030_060_05_N_P_AU_SAT_D_12
CLY_060_100_EV_N_P_AU_SAT_D_13
CLY_060_100_95_N_P_AU_SAT_D_14
CLY_060_100_05_N_P_AU_SAT_D_15
CLY_100_200_EV_N_P_AU_SAT_D_16
CLY_100_200_95_N_P_AU_SAT_D_17
CLY_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_143
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_143
https://data.csiro.au/dap/
CFG_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/CFG_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CFG_000_005_EV_N_P_AU_SAT_D_1
CFG_000_005_95_N_P_AU_SAT_D_2
CFG_000_005_05_N_P_AU_SAT_D_3
CFG_005_015_EV_N_P_AU_SAT_D_4
CFG_005_015_95_N_P_AU_SAT_D_5
CFG_005_015_05_N_P_AU_SAT_D_6
CFG_015_030_EV_N_P_AU_SAT_D_7
CFG_015_030_95_N_P_AU_SAT_D_8
CFG_015_030_05_N_P_AU_SAT_D_9
CFG_030_060_EV_N_P_AU_SAT_D_10
CFG_030_060_95_N_P_AU_SAT_D_11
CFG_030_060_05_N_P_AU_SAT_D_12
CFG_060_100_EV_N_P_AU_SAT_D_13
CFG_060_100_95_N_P_AU_SAT_D_14
CFG_060_100_05_N_P_AU_SAT_D_15
CFG_100_200_EV_N_P_AU_SAT_D_16
CFG_100_200_95_N_P_AU_SAT_D_17
CFG_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_144
2018-03-19T04:48:16Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_144
https://data.csiro.au/dap/
SND_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SND_100_200_05_N_P_AU_SAT_D
layers
SND_100_200_95_N_P_AU_SAT_D
layers
SND_100_200_EV_N_P_AU_SAT_D
layers
SND_060_100_05_N_P_AU_SAT_D
layers
SND_060_100_95_N_P_AU_SAT_D
layers
SND_060_100_EV_N_P_AU_SAT_D
layers
SND_030_060_05_N_P_AU_SAT_D
layers
SND_030_060_95_N_P_AU_SAT_D
layers
SND_030_060_EV_N_P_AU_SAT_D
layers
SND_015_030_05_N_P_AU_SAT_D
layers
SND_015_030_95_N_P_AU_SAT_D
layers
SND_015_030_EV_N_P_AU_SAT_D
layers
SND_005_015_05_N_P_AU_SAT_D
layers
SND_005_015_95_N_P_AU_SAT_D
layers
SND_005_015_EV_N_P_AU_SAT_D
layers
SND_000_005_05_N_P_AU_SAT_D
layers
SND_000_005_95_N_P_AU_SAT_D
layers
SND_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
Sand content is defined as the 20 um - 2 mm mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_145
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_145
https://data.csiro.au/dap/
AWC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_SAT_D/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
AWC_000_005_EV_N_P_AU_SAT_D_1
AWC_000_005_95_N_P_AU_SAT_D_2
AWC_000_005_05_N_P_AU_SAT_D_3
AWC_005_015_EV_N_P_AU_SAT_D_4
AWC_005_015_95_N_P_AU_SAT_D_5
AWC_005_015_05_N_P_AU_SAT_D_6
AWC_015_030_EV_N_P_AU_SAT_D_7
AWC_015_030_95_N_P_AU_SAT_D_8
AWC_015_030_05_N_P_AU_SAT_D_9
AWC_030_060_EV_N_P_AU_SAT_D_10
AWC_030_060_95_N_P_AU_SAT_D_11
AWC_030_060_05_N_P_AU_SAT_D_12
AWC_060_100_EV_N_P_AU_SAT_D_13
AWC_060_100_95_N_P_AU_SAT_D_14
AWC_060_100_05_N_P_AU_SAT_D_15
AWC_100_200_EV_N_P_AU_SAT_D_16
AWC_100_200_95_N_P_AU_SAT_D_17
AWC_100_200_05_N_P_AU_SAT_D_18
northlimit=-31.5212500001464; southlimit=-38.1295833335863; westlimit=131.5870833337; eastLimit=141.017916667185; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
csiro:service_146
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_146
https://data.csiro.au/dap/
ECD_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/ECD_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
ECD_100_200_05_N_P_AU_SAT_D
layers
ECD_100_200_95_N_P_AU_SAT_D
layers
ECD_100_200_EV_N_P_AU_SAT_D
layers
ECD_060_100_05_N_P_AU_SAT_D
layers
ECD_060_100_95_N_P_AU_SAT_D
layers
ECD_060_100_EV_N_P_AU_SAT_D
layers
ECD_030_060_05_N_P_AU_SAT_D
layers
ECD_030_060_95_N_P_AU_SAT_D
layers
ECD_030_060_EV_N_P_AU_SAT_D
layers
ECD_015_030_05_N_P_AU_SAT_D
layers
ECD_015_030_95_N_P_AU_SAT_D
layers
ECD_015_030_EV_N_P_AU_SAT_D
layers
ECD_005_015_05_N_P_AU_SAT_D
layers
ECD_005_015_95_N_P_AU_SAT_D
layers
ECD_005_015_EV_N_P_AU_SAT_D
layers
ECD_000_005_05_N_P_AU_SAT_D
layers
ECD_000_005_95_N_P_AU_SAT_D
layers
ECD_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_127
2018-03-19T04:48:16Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_127
https://data.csiro.au/dap/
PHC_ACLEP_AU_SAT_D
http://www.asris.csiro.au/arcgis/services/TERN/PHC_ACLEP_AU_SAT_D/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
PHC_100_200_05_N_P_AU_SAT_D
layers
PHC_100_200_95_N_P_AU_SAT_D
layers
PHC_100_200_EV_N_P_AU_SAT_D
layers
PHC_060_100_05_N_P_AU_SAT_D
layers
PHC_060_100_95_N_P_AU_SAT_D
layers
PHC_060_100_EV_N_P_AU_SAT_D
layers
PHC_030_060_05_N_P_AU_SAT_D
layers
PHC_030_060_95_N_P_AU_SAT_D
layers
PHC_030_060_EV_N_P_AU_SAT_D
layers
PHC_015_030_05_N_P_AU_SAT_D
layers
PHC_015_030_95_N_P_AU_SAT_D
layers
PHC_015_030_EV_N_P_AU_SAT_D
layers
PHC_005_015_05_N_P_AU_SAT_D
layers
PHC_005_015_95_N_P_AU_SAT_D
layers
PHC_005_015_EV_N_P_AU_SAT_D
layers
PHC_000_005_05_N_P_AU_SAT_D
layers
PHC_000_005_95_N_P_AU_SAT_D
layers
PHC_000_005_EV_N_P_AU_SAT_D
layers
northlimit=-31.52125; southlimit=-38.129583; westlimit=131.587083; eastLimit=141.017917; projection=WGS84
csiro:b621cfd1-8ae0-4ab9-b8ce-1164242c2c32
102.100.100/16127
pH in Calcium Chloride is defined as the of 1:5 soil/0.01M calcium chloride extract. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_147
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_147
https://data.csiro.au/dap/
AWC_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
AWC_000_005_EV_N_P_AU_TRN_N_1
AWC_000_005_95_N_P_AU_TRN_N_2
AWC_000_005_05_N_P_AU_TRN_N_3
AWC_005_015_EV_N_P_AU_TRN_N_4
AWC_005_015_95_N_P_AU_TRN_N_5
AWC_005_015_05_N_P_AU_TRN_N_6
AWC_015_030_EV_N_P_AU_TRN_N_7
AWC_015_030_95_N_P_AU_TRN_N_8
AWC_015_030_05_N_P_AU_TRN_N_9
AWC_030_060_EV_N_P_AU_TRN_N_10
AWC_030_060_95_N_P_AU_TRN_N_11
AWC_030_060_05_N_P_AU_TRN_N_12
AWC_060_100_EV_N_P_AU_TRN_N_13
AWC_060_100_95_N_P_AU_TRN_N_14
AWC_060_100_05_N_P_AU_TRN_N_15
AWC_100_200_EV_N_P_AU_TRN_N_16
AWC_100_200_95_N_P_AU_TRN_N_17
AWC_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_148
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_148
https://data.csiro.au/dap/
ECE_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/ECE_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
ECE_100_200_05_N_P_AU_TRN_N
layers
ECE_100_200_95_N_P_AU_TRN_N
layers
ECE_100_200_EV_N_P_AU_TRN_N
layers
ECE_060_100_05_N_P_AU_TRN_N
layers
ECE_060_100_95_N_P_AU_TRN_N
layers
ECE_060_100_EV_N_P_AU_TRN_N
layers
ECE_030_060_05_N_P_AU_TRN_N
layers
ECE_030_060_95_N_P_AU_TRN_N
layers
ECE_030_060_EV_N_P_AU_TRN_N
layers
ECE_015_030_05_N_P_AU_TRN_N
layers
ECE_015_030_95_N_P_AU_TRN_N
layers
ECE_015_030_EV_N_P_AU_TRN_N
layers
ECE_005_015_05_N_P_AU_TRN_N
layers
ECE_005_015_95_N_P_AU_TRN_N
layers
ECE_005_015_EV_N_P_AU_TRN_N
layers
ECE_000_005_05_N_P_AU_TRN_N
layers
ECE_000_005_95_N_P_AU_TRN_N
layers
ECE_000_005_EV_N_P_AU_TRN_N
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
Effective Cation Exchange Capacity is defined as the concentration of cations extracted using barium chloride (BaCl2) plus exchangeable H + Al. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_149
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_149
https://data.csiro.au/dap/
ECE_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/ECE_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
ECE_000_005_EV_N_P_AU_TRN_N_1
ECE_000_005_95_N_P_AU_TRN_N_2
ECE_000_005_05_N_P_AU_TRN_N_3
ECE_005_015_EV_N_P_AU_TRN_N_4
ECE_005_015_95_N_P_AU_TRN_N_5
ECE_005_015_05_N_P_AU_TRN_N_6
ECE_015_030_EV_N_P_AU_TRN_N_7
ECE_015_030_95_N_P_AU_TRN_N_8
ECE_015_030_05_N_P_AU_TRN_N_9
ECE_030_060_EV_N_P_AU_TRN_N_10
ECE_030_060_95_N_P_AU_TRN_N_11
ECE_030_060_05_N_P_AU_TRN_N_12
ECE_060_100_EV_N_P_AU_TRN_N_13
ECE_060_100_95_N_P_AU_TRN_N_14
ECE_060_100_05_N_P_AU_TRN_N_15
ECE_100_200_EV_N_P_AU_TRN_N_16
ECE_100_200_95_N_P_AU_TRN_N_17
ECE_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_150
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_150
https://data.csiro.au/dap/
CLY_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
CLY_000_005_EV_N_P_AU_TRN_N_1
CLY_000_005_95_N_P_AU_TRN_N_2
CLY_000_005_05_N_P_AU_TRN_N_3
CLY_005_015_EV_N_P_AU_TRN_N_4
CLY_005_015_95_N_P_AU_TRN_N_5
CLY_005_015_05_N_P_AU_TRN_N_6
CLY_015_030_EV_N_P_AU_TRN_N_7
CLY_015_030_95_N_P_AU_TRN_N_8
CLY_015_030_05_N_P_AU_TRN_N_9
CLY_030_060_EV_N_P_AU_TRN_N_10
CLY_030_060_95_N_P_AU_TRN_N_11
CLY_030_060_05_N_P_AU_TRN_N_12
CLY_060_100_EV_N_P_AU_TRN_N_13
CLY_060_100_95_N_P_AU_TRN_N_14
CLY_060_100_05_N_P_AU_TRN_N_15
CLY_100_200_EV_N_P_AU_TRN_N_16
CLY_100_200_95_N_P_AU_TRN_N_17
CLY_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_151
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_151
https://data.csiro.au/dap/
SLT_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SLT_100_200_05_N_P_AU_TRN_N
layers
SLT_100_200_95_N_P_AU_TRN_N
layers
SLT_100_200_EV_N_P_AU_TRN_N
layers
SLT_060_100_05_N_P_AU_TRN_N
layers
SLT_060_100_95_N_P_AU_TRN_N
layers
SLT_060_100_EV_N_P_AU_TRN_N
layers
SLT_030_060_05_N_P_AU_TRN_N
layers
SLT_030_060_95_N_P_AU_TRN_N
layers
SLT_030_060_EV_N_P_AU_TRN_N
layers
SLT_015_030_05_N_P_AU_TRN_N
layers
SLT_015_030_95_N_P_AU_TRN_N
layers
SLT_015_030_EV_N_P_AU_TRN_N
layers
SLT_005_015_05_N_P_AU_TRN_N
layers
SLT_005_015_95_N_P_AU_TRN_N
layers
SLT_005_015_EV_N_P_AU_TRN_N
layers
SLT_000_005_05_N_P_AU_TRN_N
layers
SLT_000_005_95_N_P_AU_TRN_N
layers
SLT_000_005_EV_N_P_AU_TRN_N
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
Silt content is defined as the 2 - 20 um mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_152
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_152
https://data.csiro.au/dap/
PHC_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/PHC_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
PHC_100_200_05_N_P_AU_TRN_N
layers
PHC_100_200_95_N_P_AU_TRN_N
layers
PHC_100_200_EV_N_P_AU_TRN_N
layers
PHC_060_100_05_N_P_AU_TRN_N
layers
PHC_060_100_95_N_P_AU_TRN_N
layers
PHC_060_100_EV_N_P_AU_TRN_N
layers
PHC_030_060_05_N_P_AU_TRN_N
layers
PHC_030_060_95_N_P_AU_TRN_N
layers
PHC_030_060_EV_N_P_AU_TRN_N
layers
PHC_015_030_05_N_P_AU_TRN_N
layers
PHC_015_030_95_N_P_AU_TRN_N
layers
PHC_015_030_EV_N_P_AU_TRN_N
layers
PHC_005_015_05_N_P_AU_TRN_N
layers
PHC_005_015_95_N_P_AU_TRN_N
layers
PHC_005_015_EV_N_P_AU_TRN_N
layers
PHC_000_005_05_N_P_AU_TRN_N
layers
PHC_000_005_95_N_P_AU_TRN_N
layers
PHC_000_005_EV_N_P_AU_TRN_N
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
pH in Calcium Chloride is defined as the of 1:5 soil/0.01M calcium chloride extract. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_153
2018-03-19T12:30:13Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_153
https://data.csiro.au/dap/
NTO_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/NTO_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
NTO_100_200_05_N_P_AU_TRN_N
layers
NTO_100_200_95_N_P_AU_TRN_N
layers
NTO_100_200_EV_N_P_AU_TRN_N
layers
NTO_060_100_05_N_P_AU_TRN_N
layers
NTO_060_100_95_N_P_AU_TRN_N
layers
NTO_060_100_EV_N_P_AU_TRN_N
layers
NTO_030_060_05_N_P_AU_TRN_N
layers
NTO_030_060_95_N_P_AU_TRN_N
layers
NTO_030_060_EV_N_P_AU_TRN_N
layers
NTO_015_030_05_N_P_AU_TRN_N
layers
NTO_015_030_95_N_P_AU_TRN_N
layers
NTO_015_030_EV_N_P_AU_TRN_N
layers
NTO_005_015_05_N_P_AU_TRN_N
layers
NTO_005_015_95_N_P_AU_TRN_N
layers
NTO_005_015_EV_N_P_AU_TRN_N
layers
NTO_000_005_05_N_P_AU_TRN_N
layers
NTO_000_005_95_N_P_AU_TRN_N
layers
NTO_000_005_EV_N_P_AU_TRN_N
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
WMS
csiro:service_154
2018-03-19T12:30:13Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_154
https://data.csiro.au/dap/
PTO_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/PTO_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
PTO_100_200_05_N_P_AU_TRN_N
layers
PTO_100_200_95_N_P_AU_TRN_N
layers
PTO_100_200_EV_N_P_AU_TRN_N
layers
PTO_060_100_05_N_P_AU_TRN_N
layers
PTO_060_100_95_N_P_AU_TRN_N
layers
PTO_060_100_EV_N_P_AU_TRN_N
layers
PTO_030_060_05_N_P_AU_TRN_N
layers
PTO_030_060_95_N_P_AU_TRN_N
layers
PTO_030_060_EV_N_P_AU_TRN_N
layers
PTO_015_030_05_N_P_AU_TRN_N
layers
PTO_015_030_95_N_P_AU_TRN_N
layers
PTO_015_030_EV_N_P_AU_TRN_N
layers
PTO_005_015_05_N_P_AU_TRN_N
layers
PTO_005_015_95_N_P_AU_TRN_N
layers
PTO_005_015_EV_N_P_AU_TRN_N
layers
PTO_000_005_05_N_P_AU_TRN_N
layers
PTO_000_005_95_N_P_AU_TRN_N
layers
PTO_000_005_EV_N_P_AU_TRN_N
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_155
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_155
https://data.csiro.au/dap/
BDW_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
BDW_000_005_EV_N_P_AU_TRN_N_1
BDW_000_005_95_N_P_AU_TRN_N_2
BDW_000_005_05_N_P_AU_TRN_N_3
BDW_005_015_EV_N_P_AU_TRN_N_4
BDW_005_015_95_N_P_AU_TRN_N_5
BDW_005_015_05_N_P_AU_TRN_N_6
BDW_015_030_EV_N_P_AU_TRN_N_7
BDW_015_030_95_N_P_AU_TRN_N_8
BDW_015_030_05_N_P_AU_TRN_N_9
BDW_030_060_EV_N_P_AU_TRN_N_10
BDW_030_060_95_N_P_AU_TRN_N_11
BDW_030_060_05_N_P_AU_TRN_N_12
BDW_060_100_EV_N_P_AU_TRN_N_13
BDW_060_100_95_N_P_AU_TRN_N_14
BDW_060_100_05_N_P_AU_TRN_N_15
BDW_100_200_EV_N_P_AU_TRN_N_16
BDW_100_200_95_N_P_AU_TRN_N_17
BDW_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_156
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_156
https://data.csiro.au/dap/
NTO_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/NTO_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
NTO_000_005_EV_N_P_AU_TRN_N_1
NTO_000_005_95_N_P_AU_TRN_N_2
NTO_000_005_05_N_P_AU_TRN_N_3
NTO_005_015_EV_N_P_AU_TRN_N_4
NTO_005_015_95_N_P_AU_TRN_N_5
NTO_005_015_05_N_P_AU_TRN_N_6
NTO_015_030_EV_N_P_AU_TRN_N_7
NTO_015_030_95_N_P_AU_TRN_N_8
NTO_015_030_05_N_P_AU_TRN_N_9
NTO_030_060_EV_N_P_AU_TRN_N_10
NTO_030_060_95_N_P_AU_TRN_N_11
NTO_030_060_05_N_P_AU_TRN_N_12
NTO_060_100_EV_N_P_AU_TRN_N_13
NTO_060_100_95_N_P_AU_TRN_N_14
NTO_060_100_05_N_P_AU_TRN_N_15
NTO_100_200_EV_N_P_AU_TRN_N_16
NTO_100_200_95_N_P_AU_TRN_N_17
NTO_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_157
2018-03-19T12:30:13Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_157
https://data.csiro.au/dap/
SND_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
SND_100_200_05_N_P_AU_TRN_N
layers
SND_100_200_95_N_P_AU_TRN_N
layers
SND_100_200_EV_N_P_AU_TRN_N
layers
SND_060_100_05_N_P_AU_TRN_N
layers
SND_060_100_95_N_P_AU_TRN_N
layers
SND_060_100_EV_N_P_AU_TRN_N
layers
SND_030_060_05_N_P_AU_TRN_N
layers
SND_030_060_95_N_P_AU_TRN_N
layers
SND_030_060_EV_N_P_AU_TRN_N
layers
SND_015_030_05_N_P_AU_TRN_N
layers
SND_015_030_95_N_P_AU_TRN_N
layers
SND_015_030_EV_N_P_AU_TRN_N
layers
SND_005_015_05_N_P_AU_TRN_N
layers
SND_005_015_95_N_P_AU_TRN_N
layers
SND_005_015_EV_N_P_AU_TRN_N
layers
SND_000_005_05_N_P_AU_TRN_N
layers
SND_000_005_95_N_P_AU_TRN_N
layers
SND_000_005_EV_N_P_AU_TRN_N
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
Sand content is defined as the 20 um - 2 mm mass fraction of the less than 2 mm soil material. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_158
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_158
https://data.csiro.au/dap/
SND_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/SND_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
SND_000_005_EV_N_P_AU_TRN_N_1
SND_000_005_95_N_P_AU_TRN_N_2
SND_000_005_05_N_P_AU_TRN_N_3
SND_005_015_EV_N_P_AU_TRN_N_4
SND_005_015_95_N_P_AU_TRN_N_5
SND_005_015_05_N_P_AU_TRN_N_6
SND_015_030_EV_N_P_AU_TRN_N_7
SND_015_030_95_N_P_AU_TRN_N_8
SND_015_030_05_N_P_AU_TRN_N_9
SND_030_060_EV_N_P_AU_TRN_N_10
SND_030_060_95_N_P_AU_TRN_N_11
SND_030_060_05_N_P_AU_TRN_N_12
SND_060_100_EV_N_P_AU_TRN_N_13
SND_060_100_95_N_P_AU_TRN_N_14
SND_060_100_05_N_P_AU_TRN_N_15
SND_100_200_EV_N_P_AU_TRN_N_16
SND_100_200_95_N_P_AU_TRN_N_17
SND_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_159
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_159
https://data.csiro.au/dap/
AWC_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/AWC_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
AWC_100_200_05_N_P_AU_TRN_N
layers
AWC_100_200_95_N_P_AU_TRN_N
layers
AWC_100_200_EV_N_P_AU_TRN_N
layers
AWC_060_100_05_N_P_AU_TRN_N
layers
AWC_060_100_95_N_P_AU_TRN_N
layers
AWC_060_100_EV_N_P_AU_TRN_N
layers
AWC_030_060_05_N_P_AU_TRN_N
layers
AWC_030_060_95_N_P_AU_TRN_N
layers
AWC_030_060_EV_N_P_AU_TRN_N
layers
AWC_015_030_05_N_P_AU_TRN_N
layers
AWC_015_030_95_N_P_AU_TRN_N
layers
AWC_015_030_EV_N_P_AU_TRN_N
layers
AWC_005_015_05_N_P_AU_TRN_N
layers
AWC_005_015_95_N_P_AU_TRN_N
layers
AWC_005_015_EV_N_P_AU_TRN_N
layers
AWC_000_005_05_N_P_AU_TRN_N
layers
AWC_000_005_95_N_P_AU_TRN_N
layers
AWC_000_005_EV_N_P_AU_TRN_N
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
Available Water Capacity is defined as the available water capacity computed for each of the specified depth increments. A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_160
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_160
https://data.csiro.au/dap/
CLY_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/CLY_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
CLY_100_200_05_N_P_AU_TRN_N
layers
CLY_100_200_95_N_P_AU_TRN_N
layers
CLY_100_200_EV_N_P_AU_TRN_N
layers
CLY_060_100_05_N_P_AU_TRN_N
layers
CLY_060_100_95_N_P_AU_TRN_N
layers
CLY_060_100_EV_N_P_AU_TRN_N
layers
CLY_030_060_05_N_P_AU_TRN_N
layers
CLY_030_060_95_N_P_AU_TRN_N
layers
CLY_030_060_EV_N_P_AU_TRN_N
layers
CLY_015_030_05_N_P_AU_TRN_N
layers
CLY_015_030_95_N_P_AU_TRN_N
layers
CLY_015_030_EV_N_P_AU_TRN_N
layers
CLY_005_015_05_N_P_AU_TRN_N
layers
CLY_005_015_95_N_P_AU_TRN_N
layers
CLY_005_015_EV_N_P_AU_TRN_N
layers
CLY_000_005_05_N_P_AU_TRN_N
layers
CLY_000_005_95_N_P_AU_TRN_N
layers
CLY_000_005_EV_N_P_AU_TRN_N
layers
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csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
WMS
csiro:service_161
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_161
https://data.csiro.au/dap/
SLT_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/SLT_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
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SLT_015_030_95_N_P_AU_TRN_N_8
SLT_015_030_05_N_P_AU_TRN_N_9
SLT_030_060_EV_N_P_AU_TRN_N_10
SLT_030_060_95_N_P_AU_TRN_N_11
SLT_030_060_05_N_P_AU_TRN_N_12
SLT_060_100_EV_N_P_AU_TRN_N_13
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SLT_060_100_05_N_P_AU_TRN_N_15
SLT_100_200_EV_N_P_AU_TRN_N_16
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csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_162
2018-03-19T12:30:13Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_162
https://data.csiro.au/dap/
PTO_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/PTO_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
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request
GetCoverage
request
PTO_000_005_EV_N_P_AU_TRN_N_1
PTO_000_005_95_N_P_AU_TRN_N_2
PTO_000_005_05_N_P_AU_TRN_N_3
PTO_005_015_EV_N_P_AU_TRN_N_4
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PTO_015_030_EV_N_P_AU_TRN_N_7
PTO_015_030_95_N_P_AU_TRN_N_8
PTO_015_030_05_N_P_AU_TRN_N_9
PTO_030_060_EV_N_P_AU_TRN_N_10
PTO_030_060_95_N_P_AU_TRN_N_11
PTO_030_060_05_N_P_AU_TRN_N_12
PTO_060_100_EV_N_P_AU_TRN_N_13
PTO_060_100_95_N_P_AU_TRN_N_14
PTO_060_100_05_N_P_AU_TRN_N_15
PTO_100_200_EV_N_P_AU_TRN_N_16
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PTO_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:service_163
2018-03-19T12:30:13Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_163
https://data.csiro.au/dap/
BDW_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_TRN_N/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
BDW_100_200_05_N_P_AU_TRN_N
layers
BDW_100_200_95_N_P_AU_TRN_N
layers
BDW_100_200_EV_N_P_AU_TRN_N
layers
BDW_060_100_05_N_P_AU_TRN_N
layers
BDW_060_100_95_N_P_AU_TRN_N
layers
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layers
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layers
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BDW_015_030_05_N_P_AU_TRN_N
layers
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layers
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layers
BDW_005_015_05_N_P_AU_TRN_N
layers
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csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:service_164
2018-03-19T12:30:13Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_164
https://data.csiro.au/dap/
PHC_ACLEP_AU_TRN_N
http://www.asris.csiro.au/arcgis/services/TERN/PHC_ACLEP_AU_TRN_N/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
PHC_000_005_EV_N_P_AU_TRN_N_1
PHC_000_005_95_N_P_AU_TRN_N_2
PHC_000_005_05_N_P_AU_TRN_N_3
PHC_005_015_EV_N_P_AU_TRN_N_4
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PHC_060_100_95_N_P_AU_TRN_N_14
PHC_060_100_05_N_P_AU_TRN_N_15
PHC_100_200_EV_N_P_AU_TRN_N_16
PHC_100_200_95_N_P_AU_TRN_N_17
PHC_100_200_05_N_P_AU_TRN_N_18
northlimit=-10.0004166664663; southlimit=-44.0004166670144; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/19200
csiro:30413
2018-03-28T05:01:53Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/70024
https://data.csiro.au/dap/
102.100.100/70024
10.4225/08/5abb208d8de9f
Update of the Australian Soil Classification orders map with visible-near infrared spectroscopy and digital soil class mapping
http://hdl.handle.net/102.100.100/70024?index=1
northlimit=-9.98125; southlimit=-44.00125; westlimit=112.99875; eastLimit=154.01625; projection=WGS84
10.4225/08/5abb208d8de9f
Update of the Australian Soil Classification orders map with visible-near infrared spectroscopy and digital soil class mapping
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Viscarra Rossel
Raphael
Teng
Hongfen
Zhou
Shi
Thorsten
Behrens
2018-03-28
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
csiro:R-01211_IRP
csiro:PartyGroup
TERN_Soils
TERN_Soils_DSM
Soil visible-near infrared spectra
Digital soil mapping
Soil mapping
Soil classification
Random forests
050399
Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1×1 km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders.
https://doi.org/10.1016/j.catena.2018.01.015
Teng H, Viscarra Rossel RA, Shi Z and Behrens T (2018) Updating a national soil classification with spectroscopic predictions and digital soil mapping. CATENA 164, 125-134. doi:10.1016/j.catena.2018.01.015
Teng H, Viscarra Rossel RA, Shi Z and Behrens T (2018) Updating a national soil classification with spectroscopic predictions and digital soil mapping. CATENA 164, 125-134. doi:10.1016/j.catena.2018.01.015
https://data.csiro.au/dap/search?kw=TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/dap/search?kw=TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
All Rights (including copyright) CSIRO 2018.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:40340
2021-08-03T05:42:59Z
external_system_set_TERN_SOILS
external_system_set_RDA
external_system_set_ALA
102.100.100/264762
https://data.csiro.au/dap/
102.100.100/264762
10.25919/5f1632a855c17
Atlas of Australian Soils (digital)
http://hdl.handle.net/102.100.100/264762?index=1
1960-01-01
1991-01-01
northlimit=-9.0; southlimit=-44.0; westlimit=112.0; eastLimit=154.0; projection=WGS84
10.25919/5f1632a855c17
Atlas of Australian Soils (digital)
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
CSIRO, Australia
National Resource Information Centre, BRS, Australia
2021-08-03
csiro:PartyGroup
csiro:PartyGroup
Soil Australia Northcote Atlas
050399
The Atlas of Australian Soils (Northcote et al, 1960-68) was compiled by CSIRO in the 1960's to provide a consistent national description of Australia's soils. It comprises a series of ten maps and associated explanatory notes, compiled by K.H. Northcote and others. The maps are published at a scale of 1:2,000,000, but the original compilation was at scales from 1:250,000 to 1:500,000.
Mapped units in the Atlas are soil landscapes, usually comprising a number of soil types. The explanatory notes include descriptions of soils landscapes and component soils. Soil classification for the Atlas is based on the Factual Key.
The Factual Key (Northcote 1979) was the most widely used soil classification scheme prior to the Australian Soil Classification (Isbell 2002). It dates from 1960 and was essentially based on a set of about 500 profiles largely from south-eastern Australia. It is an hierarchical scheme with 5 levels, the most detailed of which is the principal profile form (PPF). Most of the keying attributes are physical soil characteristics, and can be determined in the field.
A number of map unit interpretations have been developed to assist with national perspectives on soil information. They are also available for download.
1. Interpretations of soil properties based on the dominant Northcote classification (1992): The first set of interpretations of soil properties for the dominant soil of each landscape. Soil permeability, water holding capacity, texture, reaction trend, nutrient response and depth characteristics are assigned to relative classes. Report and many caveats are included.
2. Australian Soil Classification conversion (1996): A table that converts the Atlas of Australian Soils mapping units to an Australian Soil Classification soil Order was compiled to aid the production of Concepts and rationale of the Australian Soil Classification. Caveats and colours included.
3. Estimations of soil properties based on the dominant Northcote classification: McKenzie et al (2000) compiled tables estimating typical ranges for soil properties associated with each principal profile form (PPF) of the Factual Key. These tables were intended for use with the Atlas of Australian Soils, to provide estimates of specific soil properties for each map-unit.
Interpretations for each soil type were based on the range observed in approximately 7000 soil profiles held within the CSIRO National Soil Database, with ancillary data from Northcote et al. (1975). The systematic structure of the Factual Key makes interpolation between soil classes relatively straightforward. Soil properties were estimated using a simple two-layer model of the soil consisting of an A and B horizon. The following properties have been estimated for both the A and B horizon: horizon thickness, texture, clay content, bulk density, grade of pedality and saturated hydraulic conductivity. The estimates of thickness, texture, bulk density and pedality have been used to estimate parameters that describe the soil water retention curve - these allow calculation of the available water capacity for each layer. Interpretations relating to the complete soil profile are presence or absence of calcrete and gross nutrient status.
Caveats on the use of these interpretative tables to predict soil properties spatially are discussed by McKenzie et al (2000). A very large proportion of soil variation within a region occurs over short distances and cannot be resolved by reconnaissance scale maps. The qualitative nature of the Atlas and restrictions associated with the classification scheme and structure of the soil-landscape model impose further constraints. Technical reported included.
In 1991, a digital version of the Atlas was created by the Bureau of Rural Science from scanned tracings of the published hardcopy maps. The Digital Atlas of Australian Soils is available as a shapefile. Additionally, there is a reliability map available, with a descriptive legend. The source of the reliability data is unknown.
The Digital version of the Atlas of Australian Soils was constructed from scanned tracings of the published hardcopy source maps, the thirteen sheets of the Atlas of Australian Soils. Use of the hard copies was necessary as the original printer's separates could not be located. The positional errors inherent in the original source maps would have been added and errors introduced by subsequent processes, beginning with the natural process of paper stretch. This was followed by the data processing steps which were, in order of execution: tracing, manual digitizing, transformation of coordinates and rubber sheeting to edge-match the digital versions of the adjacent sheets.
Interpretations of the mapping units have led to the development of look-up tables for the atlas. It is important to note the many caveats are attached to these. Tables are supplied as ascii, comma delimited. Care must be taken when joining/linking the tables to the data, due to the use of mixed case in mapping unit codes.
https://discovery.csiro.au/permalink/f/14j7iti/CSIRO2132514530001981
Explanatory notes and maps: CSIRO Library catalogue entry
Northcote, K., Australia. Division of National Mapping, & CSIRO. (1960). Atlas of Australian soils. Canberra, A.C.T.]: Commonwealth Scientific and Industrial Research Organization, in association with Melbourne University Press.
https://doi.org/10.25919/2v97-se26
Explanatory Data for Sheet 1. Port Augusta-Adelaide-Hamilton Area
Northcote, K.H. Explanatory Data for Sheet 1. Port Augusta-Adelaide-Hamilton Area. East Melbourne: CSIRO in association with Melbourne University Press; 1960. https://doi.org/10.25919/2v97-se26
https://doi.org/10.25919/agad-wg30
Explanatory Data for Sheet 2. Melbourne-Tasmania Area
Northcote, K.H. Explanatory Data for Sheet 2. Melbourne-Tasmania Area. East Melbourne: CSIRO in association with Melbourne University Press; 1962. https://doi.org/10.25919/agad-wg30
https://doi.org/10.25919/7djb-1164
Explanatory Data for Sheet 3. Sydney - Canberra - Bourke - Armidale Area
Northcote, K.H. Explanatory Data for Sheet 3. Sydney - Canberra - Bourke - Armidale Area. East Melbourne: CSIRO in association with Melbourne University Press; 1966. https://doi.org/10.25919/7djb-1164
https://doi.org/10.25919/4khy-0a76
Explanatory Data for Sheet 4. Brisbane—Charleville—Rockhampton— Clermont Area
Isbell, R.F.; Thompson, C.H.; Hubble, G.D.; Beckmann, G.G.; Paton, T.R.;
Northcote, K.H. Explanatory Data for Sheet 4. Brisbane—Charleville—Rockhampton—
Clermont Area. East Melbourne: CSIRO in association with Melbourne University Press; 1967. https://doi.org/10.25919/4khy-0a76
https://doi.org/10.25919/xdz6-at90
Explanatory Data for Sheet 5. Perth—Albany—Esperance Area
Northcote, K.H.; Bettenay, E.; Churchward, H.M.; McArthur, W.M. Explanatory Data for Sheet 5. Perth—Albany—Esperance Area. East Melbourne: CSIRO in association with Melbourne University Press; 1967. https://doi.org/10.25919/xdz6-at90
https://doi.org/10.25919/210f-ns33
Explanatory Data for Sheet 6. Meekatharra—Hamersley Range Area
Bettenay, E.; Churchward, H.M.; and W. M. McArthur, W.M.; Northcote, K.H. Explanatory Data for Sheet 6. Meekatharra—Hamersley Range Area. East Melbourne: CSIRO in conjunction with Melbourne University Press; 1967. https://doi.org/10.25919/210f-ns33
https://doi.org/10.25919/8efk-b256
Explanatory Data for Sheet 7. North Queensland
Isbell, R.F.; Webb, A.A.; Murtha, G.G.; Northcote, K.H. Explanatory Data for Sheet 7. North Queensland. East Melbourne: CSIRO in conjunction with Melbourne University Press; 1968. https://doi.org/10.25919/8efk-b256
https://doi.org/10.25919/erjt-0002
Explanatory Data for Sheet 8. Northern Part of Northern Territory
Northcote, K.H. Explanatory Data for Sheet 8. Northern Part of Northern Territory. East Melbourne: CSIRO in conjunction with Melbourne University Press; 1968. https://doi.org/10.25919/erjt-0002
https://doi.org/10.25919/xgqz-5j35
Explanatory Data for Sheet 9. Kimberley Area
By W. M. McArthur, W.M.; Wright, M.J.; Northcote, K.H. Explanatory Data for Sheet 9. Kimberley Area
. East Melbourne: CSIRO in conjunction with Melbourne University Press; 1967. https://doi.org/10.25919/xgqz-5j35
https://doi.org/10.25919/n45t-2h41
Explanatory Data for Sheet 10. Central Australia
Northcote, K.H.; Isbell, R.F.; Webb, A.A.; Murtha, G.G.; Churchward, H.M.; Bettenay, E. Explanatory Data for Sheet 10. Central Australia. Melbourne: CSIRO in conjunction with Melbourne University Press; 1968. https://doi.org/10.25919/n45t-2h41
http://hdl.handle.net/102.100.100/248791?index=1
Interpretations of the Atlas of Australian Soils (1992)
McKenzie, NJ; Hook, JRH. Interpretations of the Atlas of Australian Soils. Consulting report to the Environmental Resources Information Network (ERIN). 1992. Report No.:(94/1992): 7p. 5 refs, appendix. http://hdl.handle.net/102.100.100/248791?index=1
http://hdl.handle.net/102.100.100/205546?index=1
Estimation of soil properties using the Atlas of Australian Soils
McKenzie, N.J.; Jacquier, D.W.; Ashton, L.J.; Cresswell, H.P. Estimation of soil properties using the Atlas of Australian Soils. 2000-02. http://hdl.handle.net/102.100.100/205546?index=1
https://discovery.csiro.au/permalink/f/14j7iti/CSIRO2137227640001981
Concepts and rationale of the Australian Soil Classification: CSIRO Library catalogue entry
Isbell, R. F., McDonald, W. S., Ashton, Linda Jane, CSIRO, and Australian Collaborative Land Evaluation Program. Concepts and Rationale of the Australian Soil Classification. Canberra: Australian Collaborative Land Evaluation Program, 1997. Print.
https://discovery.csiro.au/permalink/f/14j7iti/CSIRO2136956350001981
The Australian Soil Classification (1996): CSIRO Library catalogue entry
Isbell, R. F., and CSIRO. The Australian Soil Classification. 1996. Print. Australian Soil and Land Survey Handbook Ser.; v. 4.
https://discovery.csiro.au/permalink/f/14j7iti/CSIRO2129347930001981
A Factual Key for the Recognition of Australian Soils: CSIRO Library catalogue entry
Northcote, K. H., and CSIRO. Division of Soils. A Factual Key for the Recognition of Australian Soils. Adelaide: CSIRO, 1960. Print. Divisional Report (CSIRO. Division of Soils) ; 4/60.
All Rights (including copyright) CSIRO, National Resource Information Centre, BRS 1991.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:43201
2020-05-01T08:07:51Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/342616
https://data.csiro.au/dap/
102.100.100/342616
10.25919/5eaadae091d8f
HyLogger3 Olympic Dam
http://hdl.handle.net/102.100.100/342616?index=1
2015-01-01
2020-01-01
north=-30.4759; east=136.8877; projection=WGS84
10.25919/5eaadae091d8f
HyLogger3 Olympic Dam
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Mauger
Alan
Gordon
Georgina
Laukamp
Carsten
2020-05-01
csiro:572fa36b-9da5-4f76-892d-eebbf1430a65
csiro:R-13588_IRP
csiro:PartyGroup
hyperspectral, Olympic Dam, mineralogy
040201
040306
Hyperspectral (VNIR/SWIR/TIR) drill core data set collected using HyLogger3 at GSSA's NVCL node at the Tonsley Core Library in Adelaide
Collected by GSSA and processed in TSG by GSSA
https://www.auscope.org.au/nvcl
AuScope NVCL
All Rights (including copyright) Geological Survey of South Australia 2020.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:44783
2020-06-04T00:27:37Z
external_system_set_EWR_DC
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/349349
https://data.csiro.au/dap/
102.100.100/349349
10.25919/5ed83bf55be6a
Rocklea Dome C3DMM
http://hdl.handle.net/102.100.100/349349?index=1
2009-01-01
2012-01-01
10.25919/5ed83bf55be6a
Rocklea Dome C3DMM
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Laukamp
Carsten
2020-06-04
csiro:578a5831-cdac-432b-934f-cfe51b26257d
csiro:R-00639-02-002
csiro:PartyGroup
hyperspectral, HyLogging, remote sensing, earth observation, exploration, regolith
040307
040306
040601
040201
The Rocklea Dome 3D Mineral Mapping project was conducted by the Western Australian Centre of Excellence for 3D Mineral Mapping from 2009 to 2012 to showcase the opportunities offered by existing hyperspectral remote and proximal sensing technologies for comprehensive minerals systems analyses.
Key achievements of the Rocklea Dome 3D Mineral Mapping project are (Haest et al., 2012 a,b; 2013):
•Quantification of iron oxide phases and associated mineralogy derived from hyperspectral data and validated using X-ray diffractometry and geochemistry:•iron (oxyhydr-)oxide content: RMSE of 9.1 wt % Fe
•Al clay content: RMSE 3.9 wt % Al2O3
•hematite/goethite ratio: RMSE 9.0 wt % goethite
•Spatial characterisation of vitreous vs. ochreus goethite
•Defining the Tertiary channel boundary using key mineralogical parameters, such as the kaolin crystallinity
•Modelling the iron ore resource of the Rocklea Dome CID
•Identification of drill holes that were sunk into barren (i.e. bedrock) lithologies, suggesting that about a third of drill holes could have been saved
•Detailed characterisation of clay mineralogy that is associated with distinct domains of the CID and its cover (i.e. kaolin group vs. Al-smectites vs. Fe-smectites)
•Characterisation of mineral assemblages in the Quaternary cover of the Tertiary channel (e.g. calcrete)
•Improvement of quality of mineral maps by application of vegetation unmixing methods
All of the above points showcase how hyperspectral data can be used for the whole of mine life cycle, from exploration to resource characterisation.
The Rocklea Dome 3D Mineral Mapping project was conducted by the Western Australian Centre of Excellence for 3D Mineral Mapping from 2009 to 2012 to showcase the opportunities offered by existing hyperspectral remote and proximal sensing technologies for comprehensive minerals systems analyses.
All Rights (including copyright) CSIRO 2020.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:55799
2023-01-05T03:35:10Z
external_system_set_TERN_ACEF
external_system_set_AODN
external_system_set_EWR_DC
external_system_set_PACCSAP
external_system_set_ALA
external_system_set_RDA
external_system_set_TERN_SOILS
102.100.100/443950
https://data.csiro.au/dap/
102.100.100/443950
10.25919/2vbh-cx08
FNQ_2021_V01 Voyage dataset: Feb - March 2021; Biogeochemical and hydrodynamic obervations along the river-reef continuum of estuaries in eastern Cape York, Australia
http://hdl.handle.net/102.100.100/443950?index=1
2021-02-22
2021-03-07
northlimit=-10.4971; southlimit=-16.5936; westlimit=142.1853; eastLimit=145.946; projection=WGS84
10.25919/2vbh-cx08
FNQ_2021_V01 Voyage dataset: Feb - March 2021; Biogeochemical and hydrodynamic obervations along the river-reef continuum of estuaries in eastern Cape York, Australia
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Crosswell
Joey
Carlin
Geoff
Daniel
Livsey
Hillyer
Katie
Steven
Andy
2022-08-04
csiro:645d23d1-06d0-440c-98bb-90a2186698a2
csiro:R-17183_IRP
csiro:PartyGroup
Biogeochemistry
hydrodynamics
carbon
nutrients
sediments
acoustics
currents
water quality
particle size
040305
040502
040608
040599
040503
040501
040299
040607
Archive of biogeochemical and hydrodynamic data from 2021 research voyage in eastern Cape York.
Data were collected via several observational methods including an underway, flow-though sampling system, underway CTD casts, benthic lander deployments, >25hr station keeping, water column profiling, across-axis hydrodynamic transects and along-axis profiling transects. A detailed description of data, methods, and activities is included in this archive as a voyage sampling report.
Data were collected during a research voyage, which covered approximately 1200km in 14 days, including both continuous and discrete sampling while underway, at anchor stations and in specific habitats, e.g. mangroves and seagrass. Continuous measurements of surface water quality were collected by pumping water to a series of flow cells onboard the research vessel throughout the voyage. These continuous data require calibration based on discrete samples and include periodic but minor data gaps due to equipment maintenance. Underway CTD profiles of the vertical water column were simultaneously conducted across ~210km of the voyage track.
Sampling at anchor stations were conducted over full tidal cycles (>25 hrs) in three estuaries (Daintree, Normanby, Escape), with shorter anchor intervals and benthic lander deployments in four other systems (Bizant, Kennedy, Olive, and Jackey Jackey). These sampling activities included a suite of hydrodynamic and biogeochemical observations, which are grouped by specific topics and sampling methods and are described in detail in the voyage sampling summary (included).
http://zenodo.org/record/6788303#.YuNcjHZBw2w
Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef
Daniel Livsey, Ryan Turner, Peter Grace, Joseph Crosswell, & Andy Steven. (2022). Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6788303
http://doi.org/10.1029/2021JC017988
Flocculation of Riverine Sediment Draining to the Great Barrier Reef, Implications for Monitoring and Modeling of Sediment Dispersal Across Continental Shelves
Livsey, D. L., Crosswell, J. R., Turner, R. D. R., Steven, A. D. L., & Grace, P. R. (2022). Flocculation of riverine sediment draining to the Great Barrier Reef, implications for monitoring and modeling of sediment dispersal across continental shelves. Journal of Geophysical Research: Oceans, 127, e2021JC017988.
All Rights (including copyright) CSIRO 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:55731
2022-11-01T03:18:07Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/444881
https://data.csiro.au/dap/
102.100.100/444881
10.25919/nj83-a341
Soil and Landscape Grid National Soil Attribute Maps - Silt (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/444881?index=1
1950-01-01
2021-09-13
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; projection=WGS84
10.25919/nj83-a341
Soil and Landscape Grid National Soil Attribute Maps - Silt (3" resolution) - Release 2
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Searle
Ross
2022-08-22
csiro:92c0ce9a-b05f-45e8-bc4d-3a6e8e4bf115
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_575
csiro:service_577
csiro:service_576
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Silt
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 2 of the Australian Soil Silt Content product of the Soil and Landscape Grid of Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F48D6A6D48
The map gives a modelled estimate of the spatial distribution of silt in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: 2-20 um mass fraction of the < 2 mm soil material determined using the pipette method
Units: %;
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
The approach, based on machine learning, predicts each soil texture fraction at 90 m grid cell resolution, at depths 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The approach accommodates uncertainty in converting field measurements to quantitative estimates of texture fractions. Existing methods of bootstrap resampling were exploited to predict uncertainties, which are expressed as 90% prediction intervals about the mean prediction at each grid cell. The models and the prediction uncertainties were assessed by an external validation dataset. Results were compared with Version 1 Soil and Landscape Grid of Australia (v1.SLGA) (Viscarra Rossel et al. 2015). All predictive and functional accuracy diagnostics demonstrate improvements compared with v1.SLGA. Improvements were noted for the sand and clay fraction mapping with average improvement of 3% and 2%, respectively, in the RMSE estimates. Marginal improvements were made for the silt fraction mapping, which was relatively difficult to predict. We also made comparisons with recently released World Soil Grid products (v2.WSG) and made similar conclusions.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
All code is available at - https://github.com/AusSoilsDSM/SLGA/tree/master/SLGA/Development/soiltexture
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-texture
Methods Summary
Malone B & Searle R (2021)
https://www.publish.csiro.au/sr/sr20284
Detailed Methods Paper - Digital Soil Modelling
Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 2*): spatial modelling of merged field and lab measurements. Soil Research 59, 435-451.
https://doi.org/10.1071/SR20284
https://www.publish.csiro.au/sr/SR20283
Detailed Methods Paper - Data Preperation
Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 1*): re-calibration of field soil texture class centroids and description of a field soil texture conversion algorithm. Soil Research 59, 419-434.
https://doi.org/10.1071/SR2028
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:55735
2022-10-28T06:43:49Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/444877
https://data.csiro.au/dap/
102.100.100/444877
10.25919/rjmy-pa10
Soil and Landscape Grid National Soil Attribute Maps - Sand (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/444877?index=1
1950-01-01
2021-09-13
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; projection=WGS84
10.25919/rjmy-pa10
Soil and Landscape Grid National Soil Attribute Maps - Sand (3" resolution) - Release 2
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Searle
Ross
2022-08-22
csiro:769815c2-aa1f-46ee-bb29-59b60a3b4203
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_580
csiro:service_578
csiro:service_579
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Sand
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 2 of the Australian Soil Sand Content product of the Soil and Landscape Grid of Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F29646877E
The map gives a modelled estimate of the spatial distribution of sand in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: 20 um - 2 mm mass fraction of the < 2 mm soil material determined using the pipette method
Units: %;
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
The approach, based on machine learning, predicts each soil texture fraction at 90 m grid cell resolution, at depths 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The approach accommodates uncertainty in converting field measurements to quantitative estimates of texture fractions. Existing methods of bootstrap resampling were exploited to predict uncertainties, which are expressed as 90% prediction intervals about the mean prediction at each grid cell. The models and the prediction uncertainties were assessed by an external validation dataset. Results were compared with Version 1 Soil and Landscape Grid of Australia (v1.SLGA) (Viscarra Rossel et al. 2015). All predictive and functional accuracy diagnostics demonstrate improvements compared with v1.SLGA. Improvements were noted for the sand and clay fraction mapping with average improvement of 3% and 2%, respectively, in the RMSE estimates. Marginal improvements were made for the silt fraction mapping, which was relatively difficult to predict. We also made comparisons with recently released World Soil Grid products (v2.WSG) and made similar conclusions.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
All code is available at - https://github.com/AusSoilsDSM/SLGA/tree/master/SLGA/Development/soiltexture
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-texture
Methods Summary
Malone B & Searle R (2021)
https://www.publish.csiro.au/sr/sr20284
Detailed Methods Paper - Digital Soil Modelling
Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 2*): spatial modelling of merged field and lab measurements. Soil Research 59, 435-451.
https://doi.org/10.1071/SR20284
https://www.publish.csiro.au/sr/SR20283
Detailed Methods Paper - Data Preperation
Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 1*): re-calibration of field soil texture class centroids and description of a field soil texture conversion algorithm. Soil Research 59, 419-434.
https://doi.org/10.1071/SR2028
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:55739
2022-10-28T06:48:54Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/444878
https://data.csiro.au/dap/
102.100.100/444878
10.25919/djdn-5x77
Soil and Landscape Grid National Soil Attribute Maps - Soil Depth (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/444878?index=1
1950-01-01
2019-09-01
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; projection=WGS84
10.25919/djdn-5x77
Soil and Landscape Grid National Soil Attribute Maps - Soil Depth (3" resolution) - Release 2
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Searle
Ross
2022-08-22
csiro:354400cb-b5ac-491b-9b26-e6f779326732
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_583
csiro:service_582
csiro:service_584
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Soil Depth
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 2 of the Australian Soil Depth product of the Soil and Landscape Grid of Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F540FE10AA
The map gives a modelled estimate of the spatial distribution of soil depth in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: Depth of soil profile (A & B horizons)
Units: metres;
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
Rather than fitting a single model of soil thicknesses we went for a nuanced approach which entailed three separate models for:
Model 1. Predicting the occurrence of rock outcrops.
Model 2. Predicting the thickness of soils within the 0-2m range
Model 3. Predicting the occurrence of deep soils (soils greater than 2m thick)
Models 1 and 3 used the categorical model variant of the Ranger RF which was preceded by distinguishing; for Model 1, the observations that were deemed as rock outcrops from soils. And for Model 3, distinguishing soils that were less than 2m thick (and not rock outcrops) from soils greater than 2m thick. Ultimately both Models 1 and 3 were binary categorical models. 50 repeats of 5-fold CV (cross-validation) iterations of the Ranger RF model were run for each Model variant.
Model 2 used the regression form of the random forest model. After removing from the total data set the observations that were regarded as rock outcrops and soil greater than 2m, there were 111,302 observations available. Of these, 67,698 had explicitly defined soil thickness values. The remaining 43,604 were right-censored data and were treated as follows. For each repeated 5-fold iteration, prior to splitting the data in calibration and validation datasets, values from a beta function were drawn at random of length 43,604. This value (between 0 and 1) was multiplied by the censored value soil thickness and then added to this same value, creating a simulated pseudo-soil thickness. Once the simulated data were combined with actual soil thickness data, the values were square-root transformed to approximate a normal distribution. Ranger RF modelling proceeded after optimising the Hyperparameter settings as described above for the categorical modelling. Like the categorical modelling, 50 repeated 5-fold CV iterations were computed.
All three model approaches were integrated via a simple ‘if-then’ pixel-based procedure. At each pixel, if Model 1 indicated the presence of rock outcrops 45 times or more out of 50 (90% of resampling iterations), the estimated soil thickness was estimated as rock outcrop, or effectively 0cm. Similarly, for Model 3 which was the model based on prediction of deep soils (soils >2m deep). In no situations did we encounter both Models 1 and 3 predict in the positive on 90% or more occasions simultaneously. If Model 1 or 3 did not predict in the positive in 90% of iterations, the prediction outputs of Model 2 were used.
After model integration, we derived a set of soil thickness exceedance probability mapping outputs. These were derived simply by assessing the empirical probabilities (at each pixel) and then tallying the number of occasions the estimated soil depth exceeded given threshold depths of 10cm, 50cm, 100cm, and 150cm. This tallied number was divided by 50 to give an exceedance probability for each threshold depth.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
All code is available at - https://github.com/AusSoilsDSM/SLGA/tree/master/SLGA/Development/soilThickness
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-thickness
Methods Summary
Malone B & Searle R (2021)
https://www.sciencedirect.com/science/article/pii/S0016706119328630
Detailed Methods Paper - Digital Soil Modelling
Brendan Malone, Ross Searle, Improvements to the Australian national soil thickness map using an integrated data mining approach, Geoderma, Volume 377, 2020, 114579, ISSN 0016-7061.
https://doi.org/10.1016/j.geoderma.2020.114579.
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2020.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:45994
2020-08-23T22:56:19Z
external_system_set_TERN_SOILS
external_system_set_RDA
external_system_set_ALA
102.100.100/368506
https://data.csiro.au/dap/
102.100.100/368506
10.25919/5f42f2e94119c
RDF representation of ASLS Landform classification
http://hdl.handle.net/102.100.100/368506?index=1
northlimit=-10.5; southlimit=-44.416667; westlimit=112.0; eastLimit=154.5; projection=WGS84
10.25919/5f42f2e94119c
RDF representation of ASLS Landform classification
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Cox
Simon
Gregory
Linda
2020-08-24
csiro:87a0e443-97be-4897-9802-9b8a90d6af42
csiro:R-11749_IRP
csiro:PartyGroup
csiro:service_279
landform
classification
SKOS
RDF
linked data
web
120107
050104
040601
050302
Machine-readable representation of the classifiers described in chapter 5 Landform, by J.G. Speight, in Australian soil and land survey field handbook (3rd edn).
In this technique for describing landforms, the whole land surface is viewed as a mosaic of tiles of odd shapes and sizes. The scheme is intended to produce a record of observations rather than inferences.
The data in chapter 5 Landform, by J.G. Speight, in Australian soil and land survey field handbook (3rd edn) was manually converted into a SKOS representation for distribution through a Linked Data endpoint at http://registry.it.csiro.au/def/soil/au/asls/landform
http://registry.it.csiro.au/def/soil/au/asls/landform
Access classification at CSIRO's Linked Data Registry
All Rights (including copyright) CSIRO 2020.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:45995
2020-08-23T22:56:19Z
external_system_set_ALA
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/368508
https://data.csiro.au/dap/
102.100.100/368508
10.25919/5f42f324b2ef8
RDF representation of ASLS soil profile classification
http://hdl.handle.net/102.100.100/368508?index=1
northlimit=-10.5; southlimit=-44.5; westlimit=112.0; eastLimit=154.5; projection=WGS84
10.25919/5f42f324b2ef8
RDF representation of ASLS soil profile classification
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Cox
Simon
Gregory
Linda
2020-08-24
csiro:87a0e443-97be-4897-9802-9b8a90d6af42
csiro:R-11749_IRP
csiro:PartyGroup
csiro:service_278
soil
soil profile
classification
SKOS
RDF
linked data
web
050399
040601
Machine-readable representation of the classifiers described in chapter 8 Soil Profile, by R.C. McDonald and R.F. Isbell, in Australian soil and land survey field handbook (3rd edn).
A soil profile is a vertical section of a soil from the soil surface through all its horizons to parent material, other consolidated substrate material or selected depth in unconsolidated material.
The data in chapter 8 Soil Profile, by R.C. McDonald and R.F. Isbell, in Australian soil and land survey field handbook (3rd edn) was manually converted into a SKOS representation for distribution through a Linked Data endpoint at http://registry.it.csiro.au/def/soil/au/asls/landform
http://registry.it.csiro.au/def/soil/au/asls/soil-prof
Access classification at CSIRO's Linked Data Registry
All Rights (including copyright) CSIRO 2020.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:46108
2020-08-24T10:56:13Z
external_system_set_TERN_ACEF
external_system_set_AODN
external_system_set_EWR_DC
external_system_set_PACCSAP
external_system_set_ALA
external_system_set_RDA
external_system_set_TERN_SOILS
102.100.100/368593
https://data.csiro.au/dap/
102.100.100/368593
10.25919/5f439be5e60a0
Biogeochemical surveys in eastern Gulf of Carpentaria estuaries, June 2018
http://hdl.handle.net/102.100.100/368593?index=1
2018-06-13
2018-06-26
northlimit=-11.6785; southlimit=-13.5402; westlimit=141.4492; eastLimit=142.2422; projection=WGS84
10.25919/5f439be5e60a0
Biogeochemical surveys in eastern Gulf of Carpentaria estuaries, June 2018
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Crosswell
Joey
Carlin
Geoff
McLaughlin
James
Schwanger
Cassie
Stephenson
Sarah
Steven
Andy
Beale
David
Clementson
Lesley
Wojtasiewicz
Bozena
2020-08-24
csiro:e0d73159-2304-49f0-888a-7ae71b8073d8
csiro:R-90533_IRP
csiro:PartyGroup
Biogeochemistry
carbon
nutrients
sediments
water quality
particle size
metals
phytoplankton photopigments
040599
040502
040299
040608
040305
040607
040501
Archive of biogeochemical data from case study and research cruise in seven estuaries of the eastern Gulf of Carpentaria. Data were collected via two observational methods:
1) 24-36hr time series of hourly discrete samples and water-column profiles
2) Spatial surveys at approximately low tide, which include discrete samples and water-column profiles collected along the river-ocean axis of estuaries. A small number of discrete samples of porewater were also collected from bore holes to approximately 2m depth.
Datasets include carbon, nutrients, sediment particle size, metals, phytoplankton photopigments, CDOM (chromophoric dissolved organic matter), and vertical profiles of salinity, temperature, dissolved oxygen, pH, turbidity chlorophyll a fluorescence, and fluorescent dissolved organic matter (fDOM).
All Rights (including copyright) CSIRO 2020.
CSIRO Data Licence
Data is accessible online and may be reused in accordance with licence conditions
csiro:55750
2022-11-01T04:58:47Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/444882
https://data.csiro.au/dap/
102.100.100/444882
10.25919/yxjw-qm35
Soil and Landscape Grid National Soil Attribute Maps - pH (Water) (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/444882?index=1
1950-01-01
2022-05-20
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; projection=WGS84
10.25919/yxjw-qm35
Soil and Landscape Grid National Soil Attribute Maps - pH (Water) (3" resolution) - Release 1
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
2022-08-22
csiro:875b22ee-055a-4247-9e00-fb40bf4a15ce
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_605
csiro:service_606
csiro:service_607
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
pH
pH (Water)
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 1 of the Australian pH (Water) product of the Soil and Landscape Grid of Australia.
The map gives a modelled estimate of the spatial distribution of soil pH (1:5 soil water solution) in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: pH of a 1:5 soil water solution
Units: None;
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
A full description of the methods used to generate this product can be found at - https://aussoilsdsm.esoil.io/slga-version-2-products/soil-ph-15-water
We used a Random Forest model to fit the relationship between measurements and covariates. The Random Forest model uses the bootstrap resampling approach to iteratively develop the relationships between target variable and predictor variables.
Our modelling also included a repeated (n =50) bootstrap resampling approach but was different in that on each iteration the selected data which were also field data had to be converted to a ‘lab’ measurement. This ‘lab’ measurement was derived by drawing a value at random from the empirical distribution corresponding to the field measurement. In this way, we can incorporate into the modelling, the observed variability that is associated with field measurements, which also provides a seamless way to incorporate both data types.
The process of spatial modelling was relatively standard after the data integration step was done. Models were developed for each specified depth interval: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm. Our investigations also revealed there was some benefit to modelling the Random Forest model residuals using variograms. Together models were evaluated using a data set of size 10000 sites, meaning that the number of cases to evaluate models differed with each depth interval as more cases are found at the surface and near surface and drop off with increasing soil depth. We used the prediction interval coverage probability to assess the veracity of the uncertainty quantifications.
Soil pH mapping was output to the ~90m grid resolution in accordance with SLGA specifications.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Code is available at - https://github.com/AusSoilsDSM/SLGA/tree/master/SLGA/Development/phw
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-ph-15-water
Methods Summary
Malone B (2022)
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:55829
2022-10-20T05:15:18Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/444883
https://data.csiro.au/dap/
102.100.100/444883
10.25919/ejhm-c070
Soil and Landscape Grid National Soil Attribute Maps - Organic Carbon (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/444883?index=1
1970-01-01
2022-07-27
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; projection=WGS84
10.25919/ejhm-c070
Soil and Landscape Grid National Soil Attribute Maps - Organic Carbon (3" resolution) - Release 2
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Wadoux
Alexandre
Roman Dobarco
Mercedes
Malone
Brendan
Minasny
Budiman
McBratney
Alex
Searle
Ross
2022-08-22
csiro:90d10924-26b7-48ff-ac94-290418c371bf
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_620
csiro:service_621
csiro:service_622
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Organic Carbon
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 2 of the Australian Soil Organic Carbon product of the Soil and Landscape Grid of Australia.
The map gives a modelled estimate of the spatial distribution of total organic carbon in soils across Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/547523BB0801A
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: Mass fraction of carbon by weight in the < 2 mm soil material as determined by dry combustion at 900 Celcius
Units: %;
Period (temporal coverage; approximately): 1970-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
Data on total organic carbon (TOC) concentration (%) was extracted with the Soil Data Federator (https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederatorHelp.html) managed by CSIRO. The Soil Data Federator is a web API that compiles soil data from different institutions and government agencies throughout Australia. The laboratory methods for total organic carbon included in the study are 6A1, 6A1_UC, 6B2, 6B2b, 6B3, 6B3a. We selected TOC data from the period 1970-2020 to get a compromise between representativity of current TOC concentration and spatial coverage. The data was cleaned and processed to harmonize units, exclude duplicates and potentially wrong data entries (e.g. missing upper or lower horizon depths, extreme TOC values, unknown sampling date). Additional TOC measurements from the Biome of Australian Soil Environments (BASE) contextual data (Bisset et al., 2016) were also included in the analyses. TOC concentration for BASE samples was determined by the Walkley-Black method (method 6A1). Upper limits for TOC concentration by biome and land cover classes were set according to published literature, consistent datasets (Australian national Soil Carbon Research Program (SCaRP) and BASE, and data exploration to exclude unrealistic TOC values (e.g. maximum TOC = 30% in temperate forests, maximum TOC = 14% in temperate rainfed pasture). Since TOC concentration in Australian ecosystems has been underestimated by previous SOC maps, we did not set conservative TOC upper limits, knowing that machine learning model would likely underestimate high SOC values.
The equal-area quadratic spline function were fitted to the whole collection of pre-processed TOC data, and then values extracted for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm depth intervals, following GlobalSoilMap specifications (Arrouays et al., 2014}. Boxplots with TOC values by biome and land cover after data cleaning and depth standardization are shown in Figure 1.
Covariates: We collected a set of 57 spatially exhaustive environmental covariates covering Australia and representing proxies for factors influencing SOC formation and spatial distribution: soil properties, climate, organisms/vegetation, relief and parent material/age. The covariates were reprojected to WGS84 (EPSG:4326) projection and cropped to the same spatial extent. All covariates were resampled using billinear interpolation or aggregated to conform with a spatial resolution with grid cell of 90 m x 90 m.
Mapping: The spatial distribution of soil TOC concentration is driven by the combined influence of climate, vegetation, relief and parent materials. We thus modelled TOC concentration as a function of environmental covariates representing biotic and abiotic control of TOC. The measurement of SOC and their corresponding value of environmental covariate at same measurement locations were used to fit the mapping model. For the mapping we used a machine learning model called quantile regression forest.
Mapping is made with Quantile regression forest, which is similar to the popular random forest algorithm for mapping. Instead of obtaining a single statistic, that is the mean prediction from the decision trees in the random forest, we report all the target values of the leaf node of the decision trees. With QRF, the prediction is thus not a single value but a cumulative distribution of the TOC prediction at each location, which can be used to compute empirical quantile estimates.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
https://aussoilsdsm.esoil.io/slga-version-2-products/total-soil-organic-carbon-content
Methods Summary
Malone B & Searle R (2021)
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO, The University of Sydney 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:53568
2024-03-19T00:07:33Z
external_system_set_GRDC
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/434824
https://data.csiro.au/dap/
102.100.100/434824
10.25919/erjq-4g56
Evaluating 3-6 month seasonal climate forecasts for decision-making in farm case studies using APSIM
http://hdl.handle.net/102.100.100/434824
1888-12-31
2021-06-30
10.25919/erjq-4g56
Evaluating 3-6 month seasonal climate forecasts for decision-making in farm case studies using APSIM
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Meier
Elizabeth
Gaydon
Donald
2024-03-19
csiro:R-15321_IRP
csiro:PartyGroup
seasonal climate forecast
APSIM
Access-S2
ECMWF
370202
300205
300207
This data consists of APSIM v7.10 model set ups and weather files used to simulate crop production under usual practice and in response to the 3 and/or 6 month seasonal climate forecasts at 8 different case study farms. Case studies were from individual farms in the grains (including cotton), sugarcane, and rice industries in Australia (farm managers deidentified). The model setups include any modified xml files to better represent crops and other modules). The weather files include historical records obtained from the SILO data base for the nearest weather station to the farms (modified by radiation sourced from Senaps data source), 'perfect' forecasts calculated from the historical record, and seasonal climate forecasts generated from the Access-S2 and ECMWF general circulation models.
The data collection represents one package of work from the larger project "AgScore: An agricultural approach to assessing the skill of seasonal climate forecasting systems and their value for aiding on-farm decision making". In work packages 1 and 2, the methodology for downscaling a number of general circulation models (GCMs) of climate over Australia was developed and implemented. In work package 3, the methodology was used to downscale the Access-S2 and ECMWF GCM models to the location of the weather stations used to simulate the case study farms. These forecasts were used to simulate change in farm outcomes (production and gross margins) when decision-making could be informed by using a seasonal climate forecast.
Grains Research & Development Corporation (GRDC)
CSP2004-007RTX Seasonal Climate Modelling (Agscore)
All Rights (including copyright) CSIRO 2022.
Creative Commons Attribution-Noncommercial
Data is accessible online and may be reused in accordance with licence conditions
csiro:46886
2020-10-22T05:07:48Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/384422
https://data.csiro.au/dap/
102.100.100/384422
pyela python package for Exploratory Lithology Analysis - 0.6.5
http://hdl.handle.net/102.100.100/384422?index=1
2019-05-27
2019-05-27
northlimit=-10.689167; southlimit=-43.644444; westlimit=113.155; eastLimit=153.637222; projection=WGS84
csiro:46886
pyela python package for Exploratory Lithology Analysis - 0.6.5
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Perraud
Jean-Michel
Castilla
Juan
2020-10-22
csiro:ff361c92-80fc-4ba4-8028-15df0b1e0f36
csiro:PartyGroup
geology
lithology
3d-models
data-mining
facies-classification
040603
170203
080306
Python software for Exploratory Lithology Analysis
This package combines features to:
* perform natural language processing on lithology descriptions in the logs, to detect primary and secondary lithologies
* apply supervised machine learning to interpolate lithologies across a 3D grid
* visualise interactively the 3D data
https://github.com/csiro-hydrogeology/pyela
pyela repository on github
https://github.com/csiro-hydrogeology/pyela/releases/tag/0.6.5
pyela release 0.6.5
All Rights (including copyright) CSIRO 2020.
CSIRO Open Source Software Licence v1.0 (Based on MIT/BSD Open Source Licence)
Data is accessible online and may be reused in accordance with licence conditions
csiro:55684
2022-10-28T06:33:39Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/443480
https://data.csiro.au/dap/
102.100.100/443480
10.25919/hc4s-3130
Soil and Landscape Grid National Soil Attribute Maps - Clay (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/443480?index=1
1950-01-01
2021-09-13
northlimit=-9.99831; southlimit=-43.642475; westlimit=112.912468; eastLimit=153.639966; projection=WGS84
10.25919/hc4s-3130
Soil and Landscape Grid National Soil Attribute Maps - Clay (3" resolution) - Release 2
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Searle
Ross
2022-08-22
csiro:2ce74ec4-5e13-4ea8-b067-0a608988d7e7
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_609
csiro:service_610
csiro:service_608
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Clay
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
050399
This is Version 2 of the Australian Soil Clay Content product of the Soil and Landscape Grid of Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546EEE35164BF
The map gives a modelled estimate of the spatial distribution of clay in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: 2 μm mass fraction of the less than 2 mm soil material determined using the pipette method;
Units: %;
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
The approach, based on machine learning, predicts each soil texture fraction at 90 m grid cell resolution, at depths 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The approach accommodates uncertainty in converting field measurements to quantitative estimates of texture fractions. Existing methods of bootstrap resampling were exploited to predict uncertainties, which are expressed as 90% prediction intervals about the mean prediction at each grid cell. The models and the prediction uncertainties were assessed by an external validation dataset. Results were compared with Version 1 Soil and Landscape Grid of Australia (v1.SLGA) (Viscarra Rossel et al. 2015). All predictive and functional accuracy diagnostics demonstrate improvements compared with v1.SLGA. Improvements were noted for the sand and clay fraction mapping with average improvement of 3% and 2%, respectively, in the RMSE estimates. Marginal improvements were made for the silt fraction mapping, which was relatively difficult to predict. We also made comparisons with recently released World Soil Grid products (v2.WSG) and made similar conclusions.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
All code is available at - https://github.com/AusSoilsDSM/SLGA/tree/master/SLGA/Development/soiltexture
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-texture
Methods Summary
Malone B & Searle R (2021)
https://www.publish.csiro.au/sr/sr20284
Detailed Methods Paper - Digital Soil Modelling
Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 2*): spatial modelling of merged field and lab measurements. Soil Research 59, 435-451.
https://doi.org/10.1071/SR20284
https://www.publish.csiro.au/sr/SR20283
Detailed Methods Paper - Data Preperation
Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 1*): re-calibration of field soil texture class centroids and description of a field soil texture conversion algorithm. Soil Research 59, 419-434.
https://doi.org/10.1071/SR2028
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:56551
2023-09-14T21:49:14Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/448264
https://data.csiro.au/dap/
102.100.100/448264
10.25919/5qjv-7s27
Soil and Landscape Grid National Soil Attribute Maps - Organic Carbon (1" resolution) - Release 1
http://hdl.handle.net/102.100.100/448264
1970-01-01
2022-07-27
northlimit=-9.9983; southlimit=-43.6425; westlimit=112.9125; eastLimit=153.6400; projection=WGS84
10.25919/5qjv-7s27
Soil and Landscape Grid National Soil Attribute Maps - Organic Carbon (1" resolution) - Release 1
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Wadoux
Alexandre
Roman Dobarco
Mercedes
Malone
Brendan
Minasny
Budiman
McBratney
Alex
Searle
Ross
2023-09-14
csiro:90d10924-26b7-48ff-ac94-290418c371bf
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_1026
csiro:service_1027
csiro:service_1028
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Organic Carbon
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
410699
This is Version1 of the Australian Soil Organic Carbon product of the Soil and Landscape Grid of Australia at 30m resolution.
The map gives a modelled estimate of the spatial distribution of total organic carbon in soils across Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/547523BB0801A
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 1 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: Mass fraction of carbon by weight in the < 2 mm soil material as determined by dry combustion at 900 Celcius
Units: %;
Period (temporal coverage; approximately): 1970-2021;
Spatial resolution: 1 arc seconds (approx 30m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
Data on total organic carbon (TOC) concentration (%) was extracted with the Soil Data Federator (https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederatorHelp.html) managed by CSIRO. The Soil Data Federator is a web API that compiles soil data from different institutions and government agencies throughout Australia. The laboratory methods for total organic carbon included in the study are 6A1, 6A1_UC, 6B2, 6B2b, 6B3, 6B3a. We selected TOC data from the period 1970-2020 to get a compromise between representativity of current TOC concentration and spatial coverage. The data was cleaned and processed to harmonize units, exclude duplicates and potentially wrong data entries (e.g. missing upper or lower horizon depths, extreme TOC values, unknown sampling date). Additional TOC measurements from the Biome of Australian Soil Environments (BASE) contextual data (Bisset et al., 2016) were also included in the analyses. TOC concentration for BASE samples was determined by the Walkley-Black method (method 6A1). Upper limits for TOC concentration by biome and land cover classes were set according to published literature, consistent datasets (Australian national Soil Carbon Research Program (SCaRP) and BASE, and data exploration to exclude unrealistic TOC values (e.g. maximum TOC = 30% in temperate forests, maximum TOC = 14% in temperate rainfed pasture). Since TOC concentration in Australian ecosystems has been underestimated by previous SOC maps, we did not set conservative TOC upper limits, knowing that machine learning model would likely underestimate high SOC values.
The equal-area quadratic spline function were fitted to the whole collection of pre-processed TOC data, and then values extracted for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm depth intervals, following GlobalSoilMap specifications (Arrouays et al., 2014}.
Covariates: We collected a set of 57 spatially exhaustive environmental covariates covering Australia and representing proxies for factors influencing SOC formation and spatial distribution: soil properties, climate, organisms/vegetation, relief and parent material/age. The covariates were reprojected to WGS84 (EPSG:4326) projection and cropped to the same spatial extent. All covariates were resampled using billinear interpolation or aggregated to conform with a spatial resolution with grid cell of 30 m x 30 m.
Mapping: The spatial distribution of soil TOC concentration is driven by the combined influence of climate, vegetation, relief and parent materials. We thus modelled TOC concentration as a function of environmental covariates representing biotic and abiotic control of TOC. The measurement of SOC and their corresponding value of environmental covariate at same measurement locations were used to fit the mapping model.
Mapping is made with Quantile regression forest, which is similar to the popular random forest algorithm for mapping. Instead of obtaining a single statistic, that is the mean prediction from the decision trees in the random forest, we report all the target values of the leaf node of the decision trees. With QRF, the prediction is thus not a single value but a cumulative distribution of the TOC prediction at each location, which can be used to compute empirical quantile estimates.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Code - https://github.com/AusSoilsDSM/SLGA
Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html
Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/COGs
https://aussoilsdsm.esoil.io/slga-version-2-products/total-soil-organic-carbon-content
Methods Summary
Malone B & Searle R (2021)
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) The University of Sydney 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:service_950
2023-06-16T06:18:41Z
external_system_set_RDA
external_system_set_TERN_SOILS
csiro:service_950
https://data.csiro.au/dap/
BDW_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_NAT_C_V1/MapServer/WCSServer
WCS
service
1.1.2
version
GetCapabilities
request
DescribeCoverage
request
GetCoverage
request
BDW_000_005_EV_N_P_AU_NAT_C_1
BDW_000_005_95_N_P_AU_NAT_C_2
BDW_000_005_05_N_P_AU_NAT_C_3
BDW_005_015_EV_N_P_AU_NAT_C_4
BDW_005_015_95_N_P_AU_NAT_C_5
BDW_005_015_05_N_P_AU_NAT_C_6
BDW_015_030_EV_N_P_AU_NAT_C_7
BDW_015_030_95_N_P_AU_NAT_C_8
BDW_015_030_05_N_P_AU_NAT_C_9
BDW_030_060_EV_N_P_AU_NAT_C_10
BDW_030_060_95_N_P_AU_NAT_C_11
BDW_030_060_05_N_P_AU_NAT_C_12
BDW_060_100_EV_N_P_AU_NAT_C_13
BDW_060_100_95_N_P_AU_NAT_C_14
BDW_060_100_05_N_P_AU_NAT_C_15
BDW_100_200_EV_N_P_AU_NAT_C_16
BDW_100_200_95_N_P_AU_NAT_C_17
BDW_100_200_05_N_P_AU_NAT_C_18
northlimit=-10.0004166664663; southlimit=-44.0004166670142; westlimit=112.9995833334; eastLimit=153.999583334061; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16109
csiro:56602
2023-07-31T03:25:01Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/448262
https://data.csiro.au/dap/
102.100.100/448262
10.25919/c583-fd02
Soil and Landscape Grid National Soil Attribute Maps - Coarse Fragments (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/448262
1950-01-01
2021-09-13
northlimit=-9.9983; southlimit=-43.6425; westlimit=112.9125; eastLimit=153.6400; projection=WGS84
10.25919/c583-fd02
Soil and Landscape Grid National Soil Attribute Maps - Coarse Fragments (3" resolution) - Release 1
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Roman Dobarco
Mercedes
Wadoux
Alexandre
Malone
Brendan
Minasny
Budiman
McBratney
Alex
Searle
Ross
2023-07-31
csiro:d8cdb7e4-9fc9-491f-9daa-c0c0c4c78952
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_955
csiro:service_956
csiro:service_957
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Continental
Australia
DSM
Global Soil Map
spatial modelling
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
Course Fragments Probability
410699
This is Version 1 of the Soil Coarse Fragments product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. This product contains six digital soil attribute maps for each of three depth intervals, 0-5cm, 5-15cm, 15-30cm These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated using Digital Soil Mapping methods
Attribute Definition: Soil Coarse Fragments Class Probabilities as defined in the Australian Soil and Land Survey Field Handbook
Units: Probability of CF class occurring;
Period (temporal coverage; approximately): 1950-2022;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Format: Cloud Optimised GeoTIFF.
Data on the abundance of coarse fragments (particles > 2 mm) and gravimetric content (% weight) were extracted with using the the Terrestrial Ecosystem Research Network (TERN) Soil Data Federator
(https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html)
managed by CSIRO (Searle et al., 2021). The Soil Data Federator is a web API that compiles soil data from different institutions and government agencies throughout Australia. The abundance (% volume) is assessed visually in the field as part of the soil profile description using standards described in the Australian Soil and Land Survey field Handbook (National Committee on Soils and Terrain , 2009). The abundance of rock fragments per soil horizon on the cut surface of the soil profile surface of the soil horizon occupied by coarse fragments was grouped into six categories: very few (0-2 %), few (2-10 %), common (10-20 %), many (20-50 %), abundant (50-90 %) and very abundant (> 90%). The gravimetric content (% mass) is measured in the laboratory as percent mass of coarse fragments (particles > 2 mm) from the whole soil. Here, we take the profile surface abundance of coarse fragments as a proxy for volumetric coarse fragments (CFVol). The data was cleaned and processed to exclude duplicates and wrong data entries (e.g., missing values). The observations of CFVol (%) were converted into GlobalSoilMap depth intervals with the slab function of the aqp R package (Beaudette et al., 2021), assigning the most probable class to each depth interval. The gravimetric coarse fragments were also standardized to the GlobalSoilMap depth intervals with equal-area quadratic splines (Bishop et al., 1999). Observations of gravimetric coarse fragment content (〖CF〗_Weight) were transformed into volumetric with the equation:
〖CF〗_Vol (%)=〖Vol〗_CF/〖Vol〗_WhSoil (〖Weig ht〗_CF / ρ_CF)/(〖Weight〗_WhSoil /〖 ρ〗_WhSoil )=(〖CF〗_Weight×ρ_WhSoil)/ρ_CF ,
Where where ρ_WhSoil is the bulk density prediction for bulk soil from SLGA (Viscarra Rossel et al., 2014), ρ_CF is assumed to be 2.65 g cm-3 (Hurlbut and Klein (1977) in Mckenzie et al. (2002) and 〖CF〗_Vol is the volumetric coarse fragment content (continuous),which was assigned to the corresponding class. This resulted in CFVol observations for 110,308 locations.
Mapping was produces using quantile regression forest fitted with the observed coarse fragments class data and a large set of environmental variables as predictors.
Code - https://github.com/AusSoilsDSM/SLGA
Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html
Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/COGs
https://aussoilsdsm.esoil.io/slga-version-2-products/soc-fractions
Methods Summary
Mercedes Román Dobarcoa, Alexandre M.J-C. Wadoux, Brendan Malone, Budiman Minasny, Alex B. McBratney, Ross Searle (2022)
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) The University of Sydney 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:service_951
2023-06-16T06:18:41Z
external_system_set_TERN_SOILS
external_system_set_RDA
csiro:service_951
https://data.csiro.au/dap/
BDW_ACLEP_AU_NAT_C
http://www.asris.csiro.au/arcgis/services/TERN/BDW_ACLEP_AU_NAT_C_V1/MapServer/WMSServer
WMS
service
1.3.0
version
GetCapabilities
request
GetMap
request
GetFeatureInfo
request
esri_wms:GetStyles
request
BDW_100_200_05_N_P_AU_NAT_C
layers
BDW_100_200_95_N_P_AU_NAT_C
layers
BDW_100_200_EV_N_P_AU_NAT_C
layers
BDW_060_100_05_N_P_AU_NAT_C
layers
BDW_060_100_95_N_P_AU_NAT_C
layers
BDW_060_100_EV_N_P_AU_NAT_C
layers
BDW_030_060_05_N_P_AU_NAT_C
layers
BDW_030_060_95_N_P_AU_NAT_C
layers
BDW_030_060_EV_N_P_AU_NAT_C
layers
BDW_015_030_05_N_P_AU_NAT_C
layers
BDW_015_030_95_N_P_AU_NAT_C
layers
BDW_015_030_EV_N_P_AU_NAT_C
layers
BDW_005_015_05_N_P_AU_NAT_C
layers
BDW_005_015_95_N_P_AU_NAT_C
layers
BDW_005_015_EV_N_P_AU_NAT_C
layers
BDW_000_005_05_N_P_AU_NAT_C
layers
BDW_000_005_95_N_P_AU_NAT_C
layers
BDW_000_005_EV_N_P_AU_NAT_C
layers
northlimit=-10.000417; southlimit=-44.000417; westlimit=112.999583; eastLimit=153.999583; projection=WGS84
csiro:da6a44b9-0a4a-4905-a821-874573de0b09
102.100.100/16109
A collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm, consistent with the Specifications of the GlobalSoilMap.
csiro:59277
2023-06-19T01:03:44Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/486760
https://data.csiro.au/dap/
102.100.100/486760
10.25919/gxyn-pd07
Soil and Landscape Grid National Soil Attribute Maps - Bulk Density - Whole Earth - Release 2
http://hdl.handle.net/102.100.100/486760
1950-01-01
2023-06-01
northlimit=-9.9983; southlimit=-43.6425; westlimit=112.9125; eastLimit=153.6400; projection=WGS84
10.25919/gxyn-pd07
Soil and Landscape Grid National Soil Attribute Maps - Bulk Density - Whole Earth - Release 2
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
2023-06-19
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_947
csiro:service_949
csiro:service_948
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Bulk Density
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
410699
This is Version 2 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546EE212B0048
The map gives a modelled estimate of the spatial distribution of Bulk Density in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method;
Units: g/cm3;
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
An attempt was made to update digital soil mapping of whole soil bulk density for Australia. This was an update of first attempt by Viscarra Rossel et al. (2014). Based on model evaluations using a dataset not included in any modelling, the updated version (2nd Version) represents a demonstrable improvement on the 1st version.
Since the first version, more measured site data has been made available and retrievable via the Australian SoilDataFederator. In 2014 there were 3776 sites with measured whole soil bulk density. For the new update, 6116 sites had measured data. Because of usually strong empirical relationships between bulk density, soil texture and soil carbon, the use of pedotransfer functions (to predict bulk density from soil texture and soil carbon) was performed with the intention of increasing data density and spatial coverage of data that would ultimately improve digital soil mapping prediction skill. This added a further 15735 sites after building a spatial pedotransfer function using a dataset of 12308 cases (3939 sites with bulk density, soil carbon and soil texture data).
The basic steps of the work entailed.
Use soil data federator to get pertinent soils observation data
Develop spatial pedotransfer function prediction whole soil bulk density using soil carbon and texture data.
Compile measured and inferred whole soil bulk density data (86306 cases), then setting aside a dataset of 7500 cases for external model evaluation.
Predictive models using random forest algorithm with 78806 data cases fitted. To account for uncertainties in pedotransfer function inferred data, Monte Carlo simulations were performed from the pedotransfer function model. Simulation was repeated 100 times.
Predictive model uncertainties quantified using UNEEC approach (Uncertainty Estimation based on local errors and Clustering).
Quantification of model extension limits derived using hybrid method involving multivariate convex hull analysis and count of observations.
Digital soil maps with quantified uncertainties (5th and 95th prediction interval limits) and assessment of model extrapolation risk were produced at 90m resolution for the following depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Code - https://github.com/AusSoilsDSM/SLGA
Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html
Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/COGs
https://aussoilsdsm.esoil.io/slga-version-2-products/whole-soil-bulk-density
Methods Summary
Malone B & Searle R (2021)
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://data.csiro.au/browse/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia Digital Soil Property Maps
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) CSIRO 2023.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:59165
2023-11-23T00:40:14Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/486764
https://data.csiro.au/dap/
102.100.100/486764
10.25919/rrpg-m948
Daily 100 m land surface temperature generated through MODIS-Landsat fusion for 12 OzFlux regions across Australia during 2013-2021
http://hdl.handle.net/102.100.100/486764
2013-01-01
2021-12-31
northlimit=-10.0000; southlimit=-45.0000; westlimit=112.0000; eastLimit=154.0000; projection=WGS84
10.25919/rrpg-m948
Daily 100 m land surface temperature generated through MODIS-Landsat fusion for 12 OzFlux regions across Australia during 2013-2021
v3
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Yu
Yi
Renzullo
Luigi
McVicar
Tim
Malone
Brendan
Tian
Siyuan
2023-11-23
csiro:R-14062_IRP
csiro:PartyGroup
Land surface temperature
Spatiotemporal fusion
ESTARFM
Bias correction
MODIS
Landsat
ECOSTRESS
Himawari
379901
401304
This collection aims to provide land surface temperature (LST) data for benchmarking algorithmic refinements of spatiotemporal fusion approaches. It contains LST data generated through MODIS-Landsat fusion for 12 OzFlux regions across Australia during 2013-2021 (and Himawari-Landsat fusion for 3 OzFlux regions within southeast Australia during 2016-2021). The area of each region is 100 × 100 km. The spatial resolution is 100 m and the temporal frequency is daily (around 10:30 am local solar time). This collection also provides the MODIS (and Himawari) and Landsat data that were used as inputs into the fusion, as well as an independent LST collection from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) that was employed for cross-platform comparison.
Several important updates within this version:
- Updated the site description file with climate, land cover, and monthly emissivity values for each site.
- Updated the OzFlux LST data using a new strategy. This strategy does not consider daylight saving time and does explicitly claim the 'seconds' timestep in the TOI (Time of Interests). Compared to the strategy used in our RSE paper, this strategy is expected to better coincide with the satellite overpass time.
- Added MOD11A1 LST and viewtime data for the Australian continent during 2013-2021, with their native resolution of 1 km. Users can just simply clip the data to their targetted study areas.
- Added the Himawari-Landsat fusion LST data for 3 OzFlux regions within southeast Australia during 2016-2021. The data format is identical to the MODIS-Landsat fusion LST data.
** Please refer to the GitHub repository (https://github.com/yuyi13/ubESTARFM) for algorithm details and citation information. **
The LST data was generated using a variant of a well-known spatiotemporal fusion algorithm, referred to as the unbiased Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ubESTARFM). Independent validation shows that ubESTARFM LST has an unbiased root mean squared error (ubRMSE) of 2.57 K and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11,290 observations at the 12 sites, both of which are better than those calculated for its baseline version (ESTARFM), being an ubRMSE of 3.80 K and R of 0.92. When spatially compared to ECOSTRESS LST, ubESTARFM LST has an ubRMSE of 2.00 K and R of 0.70 over 43 near clear-sky scenes, while ESTARFM LST has an ubRMSE of 2.68 K and R of 0.59.
A further assessment underscored the potential of ubESTARFM for application using LST data acquired from geostationary platforms (e.g., Himawari-8), with a mean ubRMSE (R) of 2.22 K (0.97) against in-situ LST over 1327 observations at 3 sites from southeast Australia at 00:30 GMT.
http://doi.org/10.1016/j.rse.2023.113784
RSE paper
Yu, Y., Renzullo, L. J., McVicar, T. R., Malone, B. P. and Tian, S., 2023. Generating daily 100 m resolution land surface temperature estimates continentally using an unbiased spatiotemporal fusion approach. Remote Sensing of Environment, 297, 113784.
http://doi.org/10.5194/egusphere-egu23-1501
Conference talk
Yu, Y., Renzullo, L.J., Tian, S. and Malone, B., 2023. An unbiased spatiotemporal fusion approach to generate daily 100 m spatial resolution land surface temperature over a continental scale, EGU General Assembly 2023, Vienna, Austria, 24-28 April, EGU23-1501.
http://github.com/yuyi13/ubESTARFM
GitHub repository
https://doi.org/10.5281/zenodo.8017282
Code on Zenodo
All Rights (including copyright) Australian National University, CSIRO 2023.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:60983
2023-11-14T01:35:47Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/602022
https://data.csiro.au/dap/
102.100.100/602022
10.25919/gayv-1e32
3 arcsecond climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment for modelling biodiversity patterns
http://hdl.handle.net/102.100.100/602022
1976-01-01
2005-12-31
northlimit=-8.0000; southlimit=-43.7425; westlimit=112.9000; eastLimit=154.0000; projection=GDA94
10.25919/gayv-1e32
3 arcsecond climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment for modelling biodiversity patterns
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Harwood
Thomas
King
Darran
Nolan
Martin
Gallant
John
Ware
Chris
Austin
Jenet
Williams
Kristen
2023-11-14
csiro:R-08623_IRP
csiro:PartyGroup
average climate
1975
2005
1990-centred
30 year
minumum temperature
minumum temperature
precipitation
evaporation
radiation
seasonality
digital elevation model
ANUCLIM
Australia
TERN
biodiversity
NARCLIM
410401
370202
410402
A suite of 3 arsecond resolution climate surfaces for the Australian continent, with adjustment for the radiative effects of terrain. This collection represents a 30 year average centred on 1990 (ANUCLIM derived). Precipitation, temperature, evaporation and water balance data are presented as annual means or totals and maximum and minimum monthly values.
Data are provided as GeoTiffs. Spatial reference system is WGS84 geographics
The data were generated using the CSIRO 'terraformer' software. A short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files.
Climate surfaces for the present were based on the ANUCLIM 6.1 (Xu and Hutchinson, 2011) 30 year average climate surfaces for Australia (1976-2005), with elevational lapse rate correction applied over the 3 arcsecond SRTM-derived digital elevation model (Gallant et al. 2009). Radiative correction derived from the same DEM was applied to radiation and maximum temperature before calculation of evaporation, using the CSIRO TerraFormer software. Summary statistics for each variable were then calculated including variables described in appendices provided with Williams et al (2012). A short methods summary is provided in the file 9sClimateMethodsSummary.pdf (Harwood et al. 2018) for further information. Additional summary information is provided in ClimateVariables_v1.docx' (Williams et al. 2014).
These 3 arcsecond climate variables were incorporated into the TERN 90m Digital Soil Mapping Raster Covariate Stack (Searle et al. 2022).
REFERENCES
Gallant J, Wilson N, Tickle PK, Dowling T and Read A (2009) 3 second SRTM Derived Digital Elevation Model (DEM) Version 1.0. Record 1.0. Geoscience Australia, Canberra, Australia. DOI: http://pid.geoscience.gov.au/dataset/ga/69888.
Harwood T, Donohue R, Harman I, McVicar T, Ota N, Perry J and Williams K (2018) 9-second gridded climate surfaces for Australia: short summary In: Harwood T et al. (eds) 9s climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment. v3. CSIRO. Data Collection. CSIRO Land and Water, Canberra, Australia, 11. DOI: https://doi.org/10.4225/08/5afa9f7d1a552.
Williams KJ, Belbin L, Austin MP, Stein J and Ferrier S (2012) Which environmental variables should I use in my biodiversity model? International Journal of Geographic Information Sciences 26(11), 2009-2047. DOI: 10.1080/13658816.2012.698015.
Searle R, Malone B, Wilford J, Austin J, Ware C, Webb M, Roman Dobarco M and Van Niel T (2022) TERN Digital Soil Mapping Raster Covariate Stacks. CSIRO. Data Collection, Canberra, Australia. DOI: https://doi.org/10.25919/jr32-yq58.
Xu T and Hutchinson M (2011) ANUCLIM Version 6.1 User Guide. The Australian National University, Fenner School of Environment and Society, Canberra. <https://fennerschool.anu.edu.au/research/products/anuclim>.
https://fennerschool.anu.edu.au/research/products/anuclim
ANUCLIM Version 6.1
Xu T and Hutchinson M (2011) ANUCLIM Version 6.1 User Guide. The Australian National University, Fenner School of Environment and Society, Canberra. <https://fennerschool.anu.edu.au/files/anuclim61.pdf>.
https://aussoilsdsm.esoil.io/dsm-covariates/covariate-data
TERN Digital Soil Modelling covariate datasets
http://pid.geoscience.gov.au/dataset/ga/69888
3 second SRTM Derived Digital Elevation Model (DEM) Version 1.0.
Gallant J, Wilson N, Tickle PK, Dowling T and Read A (2009) 3 second SRTM Derived Digital Elevation Model (DEM) Version 1.0. Record 1.0. Geoscience Australia, Canberra, Australia. DOI: http://pid.geoscience.gov.au/dataset/ga/69888.
https://doi.org/10.25919/8ecs-g970
3 second abiotic environmental raster data for the NARCLIM region of Australia aggregated from various sources for modelling biodiversity patterns
Harwood, Tom; King, Darran; Nolan, Martin; Gallant, John; Ware, Chris; Austin, Jenet; Williams, Kristen (2018): 3 second abiotic environmental raster data for the NARCLIM region of Australia aggregated from various sources for modelling biodiversity patterns. v2. CSIRO. Data Collection. https://doi.org/10.25919/8ecs-g970
https://doi.org/10.4225/08/5afa9f7d1a552
9s climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment
Harwood, Tom; Donohue, Randall; Harman, Ian; McVicar, Tim; Ota, Noboru; Perry, Justin; Williams, Kristen (2016): 9s climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment. v3. CSIRO. Data Collection. https://doi.org/10.4225/08/5afa9f7d1a552
https://www.tandfonline.com/doi/full/10.1080/13658816.2012.698015
Which environmental variables should I use in my biodiversity model?
Williams KJ, Belbin L, Austin MP, Stein J and Ferrier S (2012) Which environmental variables should I use in my biodiversity model? International Journal of Geographic Information Sciences 26(11), 2009-2047. DOI: 10.1080/13658816.2012.698015.
https://doi.org/10.25919/jr32-yq58
TERN Digital Soil Mapping Raster Covariate Stacks
Searle, Ross; Malone, Brendan; Wilford, John; Austin, Jenet; Ware, Chris; Webb, Mathew; Roman Dobarco, Mercedes; Van Niel, Tom (2022): TERN Digital Soil Mapping Raster Covariate Stacks. v2. CSIRO. Data Collection. https://doi.org/10.25919/jr32-yq58
All Rights (including copyright) CSIRO 2018.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:60678
2023-10-17T07:18:08Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/601405
https://data.csiro.au/dap/
102.100.100/601405
10.25919/3p68-1f62
NVCL Yilgarn BIFs
http://hdl.handle.net/102.100.100/601405
2016-01-01
2016-12-31
northlimit=-27.1712; southlimit=-32.2951; westlimit=117.9815; eastLimit=124.9421; projection=WGS84
10.25919/3p68-1f62
NVCL Yilgarn BIFs
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Laukamp
Carsten
Duuring
Paul
2023-10-17
csiro:R-20224_IRP
csiro:PartyGroup
hyperspectral
geochemistry
drill core
banded iron formation
Yilgarn Craton
370505
370301
379901
Four hyperspectral drill core data sets (AuScope NVCL) and associated whole rock geochemistry (GSWA) from three BIF-hosted high-grade iron ore deposits in the Yilgarn Craton, Western Australia.
HyLogger data were collected by the Geological Survey of Western Australia (GSWA). Whole rock geochemistry for three of the four drill cores was sourced from https://wamexgeochem.net.au/dh_query_page. GSWA records were produced for of the four drill core datasets by Paul Duuring (GSWA) and Carsten Laukamp (CSIRO) in 2016.
AuScope Ltd
All Rights (including copyright) CSIRO, Geological Survey of Western Australia 2023.
Creative Commons Attribution-Noncommercial
Data is accessible online and may be reused in accordance with licence conditions
csiro:56603
2023-11-22T23:54:59Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/448266
https://data.csiro.au/dap/
102.100.100/448266
10.25919/fa46-ey49
Soil and Landscape Grid National Soil Attribute Maps - Soil Organic Carbon Fractions (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/448266
1950-01-01
2022-10-06
northlimit=-9.9983; southlimit=-43.6425; westlimit=112.9125; eastLimit=153.6400; projection=WGS84
10.25919/fa46-ey49
Soil and Landscape Grid National Soil Attribute Maps - Soil Organic Carbon Fractions (3" resolution) - Release 1
v5
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Roman Dobarco
Mercedes
Wadoux
Alexandre
Malone
Brendan
Minasny
Budiman
McBratney
Alex
Searle
Ross
2023-11-23
csiro:d8cdb7e4-9fc9-491f-9daa-c0c0c4c78952
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_1047
csiro:service_1048
csiro:service_1049
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attributes
Continental
Australia
DSM
Global Soil Map
spatial modelling
3-dimensional soil mapping
spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
Soil Organic Carbon Fractions
410699
This is Version 1 of the Soil Organic Carbon Fractions product of the Soil and Landscape Grid of Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. This product contains six digital soil attribute maps for each of three depth intervals, 0-5cm, 5-15cm, 15-30cm These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).
These maps are generated using Digital Soil Mapping methods
Attribute Definition: Soil Organic Carbon Fractions :- mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC)
Units: Various;
Period (temporal coverage; approximately): 1950-2022;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 18;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Total size before compression: about 8GB;
Total size after compression: about 4GB;
Data license : Creative Commons Attribution 4.0 (CC BY);
Format: Cloud Optimised GeoTIFF.
Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuum set of compounds with different chemical composition, origin and susceptibilities to decomposition, that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia’s soils.
The three SOC fractions: mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric log-ratio transformation (ilr) to account for the closed compositional nature of SOC fractions. The resulting , back-transformed ilr components were mapped across Australia.
SOC fraction stocks for the 0-30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments and soil thickness. Mapping was done by quantile regression forest fitted with the ilr transformed data and a large set of environmental variables as predictors.
The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59% ±17.5%, whereas 28% ± 17.5% was PyOC and 13% ± 11.1% was POC. The allocation of TOC into the MAOC fractions increased with depth. SOC vulnerability (i.e., POC/[MAOC + PyOC]) was greater in areas with Mediterranean and temperate climate. TOC and the distribution among fractions were the most influential variables on SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates importance metric. We estimated the total SOC stocks (0-30 cm) to be 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as response to land use change, management, and climate change.
Code - https://github.com/AusSoilsDSM/SLGA
Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html
Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/COGs
https://aussoilsdsm.esoil.io/slga-version-2-products/soc-fractions
Methods Summary
Mercedes Román Dobarcoa, Alexandre M.J-C. Wadoux, Brendan Malone, Budiman Minasny, Alex B. McBratney, Ross Searle (2022)
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Soil and Landscape Grid of Australia
https://data.csiro.au/browse/kw/TERN_Soils
Browse all Soil and Landscape Grid of Australia collections
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/MetaData/ASLG_File_Naming_Conventions.html
File Naming Conventions
All Rights (including copyright) The University of Sydney 2022.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:56260
2023-12-04T04:27:27Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/446811
https://data.csiro.au/dap/
102.100.100/446811
10.25919/akf1-dv08
TERN Surveillance monitoring program: Soil vis-NIR spectral library with accompanying soil measurement data for 367 specimens
http://hdl.handle.net/102.100.100/446811
2019-01-03
2022-06-30
northlimit=-10.9694; southlimit=-44.6946; westlimit=110.8650; eastLimit=157.1085; projection=WGS84
10.25919/akf1-dv08
TERN Surveillance monitoring program: Soil vis-NIR spectral library with accompanying soil measurement data for 367 specimens
v2
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Stockmann
Uta
Tuomi
Seija
Sparrow
Ben
2023-12-04
csiro:R-14062_IRP
csiro:PartyGroup
soil infrared spectroscopy
visible near infrared
soil spectral inference
chemometrics
410406
410404
410699
AusPlots is the core program delivered by TERN Surveillance. AusPlots is a plot-based surveillance monitoring program, undertaking baseline assessments of ecosystems across the country. The aim of AusPlots is to establish and maintain a national network of plots that enables consistent ecological assessment and ongoing monitoring. The AusPlots network collects a range of field data for integration with other existing data sources and current knowledge. More information about TERN Surveillance can be found at their website - https://www.tern.org.au/tern-observatory/tern-ecosystem-surveillance/
As of 2020 there are about 690 active AusPlot sites distributed across Australia.
During 2018 to 2019, most of the soil specimens collected at the AusPlots sites were scanned with an ASD portable vis-NIR spectrometer (PANalytical Inc., Boulder, CO, USA). As there is a specific field data collection protocol carried out at each site i.e., numerous spatial distributed samples collected in a grid-design of specified dimensions, this meant a substantial number of specimens to scan. In total there were 19,380 vis-nir spectra collected. The scanning was done at CSIRO Waite Campus with a CSIRO owned vis-NIR spectrometer.
Accompanying the soil vis-NIR library is a dataset of analytical soil measurements for 367 soil specimens selected the Ausplots soil specimen archive.
The condition of the soil for vis-NIR data collection was: Air-dried and ground to <2mm
Soil vis-NIR spectra were collected using a Labspec ASD portable vis-NIR spectrometer (PANalytical Inc., Boulder, CO, USA) Serial # 4103.
Measurement units of the soil vis-NIR spectra is reflectance and is output to 1nm spectral resolution. The data ranges from 350-2500nm.
In total 367 specimens were selected and characterized in the laboratory. 21 of these were randomly selected and analysed as blind duplicates. The soil variables included:
• Electrical conductivity (3A1; dS/m)
• pH (4A1; 1:5 soil:water)
• pH (4B4; 0.01M CaCl2)
• Total soil carbon (6H5; %)
• Soil organic carbon (6A1_UC; %)
• Total Nitrogen (7A8; %)
• Calcium Carbonate (19B2; %)
• Cation Exchange Capacity (15D2; Ammonium Acetate cmol(+/-)/kg)
• Soil texture, clay silt and sand (P10; %)
• Soil digestions (17A3; mg/kg): Ca, K, Mg, Na, S, Al, As, B, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, P, Pb, Sb, Se, Zn.
Lab methods associated to the codes above are described in Rayment and Lyons (2010) except for total soil carbon (6H5) and nitrogen (7AH) where Matejovic (1997) is the method reference.
References:
Rayment, G, Lyons, DJ (2010) 'Soil Chemical Methods - Australasia.' (CSIRO Publishing).
Matejovic, I., 1997. Determination of carbon and nitrogen in samples of various soils by the dry combustion. Communications in Soil Science and Plant Analysis 28(17-18), 1499-1511.
All Rights (including copyright) CSIRO 2020.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:61137
2023-12-04T04:12:22Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/602560
https://data.csiro.au/dap/
102.100.100/602560
10.25919/82gq-pf38
Soil and Landscape Grid National Soil Attribute Maps - Rock outcrop occurrence (3" resolution) - Release 1
http://hdl.handle.net/102.100.100/602560
1950-01-01
2020-01-01
northlimit=-10.3457; southlimit=-44.4739; westlimit=111.8093; eastLimit=154.7123; projection=WGS84
10.25919/82gq-pf38
Soil and Landscape Grid National Soil Attribute Maps - Rock outcrop occurrence (3" resolution) - Release 1
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Searle
Ross
2023-12-04
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_1062
csiro:service_1063
csiro:service_1064
TERN_Soils
TERN_Soils_DSM
Soil
TERN
Raster
Attribute
Soil Thickness
Rock outcropping
Outcrops
Rock
Geology
Continental DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Machine learning
Soil Maps
Digital Soil Mapping
SLGA
490501
370508
410602
The map gives a modelled estimate (probability) of the spatial distribution of rock outcroppings across Australia.
This product was produced in the development of the updated soil thickness map of Australia details of which are published in Malone and Searle (2020; https://doi.org/10.1016/j.geoderma.2020.114579). This product is the output from Model 1 of aforementioned paper and uses the Rock Properties database provided by Geoscience Australia which gives the locations of sampled rock outcrops across Australia (http://www.ga.gov.au/scientific-topics/disciplines/geophysics/rock-properties). Filtering this dataset resulted in 14616 rock outcrop locations within areas where relief >300 m. A machine learning model was used to find relationships between observed data and associated environmental covariate data to inform the mapping of rock outcrop occurrence across Australia.
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: Probability of rock outcrops
Units: 0-1
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 1;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;NA
Format: Cloud Optimised GeoTIFF;
The modelling and mapping of rock outcrop occurrence was performed as part of efforts to update and improve modelling of soil thickness across the Australia. Following is the description of method and further details of this work.
Rather than fitting a single model of soil thicknesses we went for a nuanced approach which entailed three separate models for:
Model 1. Predicting the occurrence of rock outcrops.
Model 2. Predicting the thickness of soils within the 0-2m range
Model 3. Predicting the occurrence of deep soils (soils greater than 2m thick)
Models 1 and 3 used the categorical model variant of the Ranger RF which was preceded by distinguishing; for Model 1, the observations that were deemed as rock outcrops from soils. And for Model 3, distinguishing soils that were less than 2m thick (and not rock outcrops) from soils greater than 2m thick. Ultimately both Models 1 and 3 were binary categorical models. 50 repeats of 5-fold CV (cross-validation) iterations of the Ranger RF model were run for each Model variant.
Model 2 used the regression form of the random forest model. After removing from the total data set the observations that were regarded as rock outcrops and soil greater than 2m, there were 111,302 observations available. Of these, 67,698 had explicitly defined soil thickness values. The remaining 43,604 were right-censored data and were treated as follows. For each repeated 5-fold iteration, prior to splitting the data in calibration and validation datasets, values from a beta function were drawn at random of length 43,604. This value (between 0 and 1) was multiplied by the censored value soil thickness and then added to this same value, creating a simulated pseudo-soil thickness. Once the simulated data were combined with actual soil thickness data, the values were square-root transformed to approximate a normal distribution. Ranger RF modelling proceeded after optimising the Hyperparameter settings as described above for the categorical modelling. Like the categorical modelling, 50 repeated 5-fold CV iterations were computed.
All three model approaches were integrated via a simple ‘if-then’ pixel-based procedure. At each pixel, if Model 1 indicated the presence of rock outcrops 45 times or more out of 50 (90% of resampling iterations), the estimated soil thickness was estimated as rock outcrop, or effectively 0cm. Similarly, for Model 3 which was the model based on prediction of deep soils (soils >2m deep). In no situations did we encounter both Models 1 and 3 predict in the positive on 90% or more occasions simultaneously. If Model 1 or 3 did not predict in the positive in 90% of iterations, the prediction outputs of Model 2 were used.
After model integration, we derived a set of soil thickness exceedance probability mapping outputs. These were derived simply by assessing the empirical probabilities (at each pixel) and then tallying the number of occasions the estimated soil depth exceeded given threshold depths of 10cm, 50cm, 100cm, and 150cm. This tallied number was divided by 50 to give an exceedance probability for each threshold depth.
All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Code - https://github.com/AusSoilsDSM/SLGA
Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html
Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/COGs
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-thickness
Method summary
https://www.sciencedirect.com/science/article/pii/S0016706119328630
Journal article
Brendan Malone, Ross Searle,
Improvements to the Australian national soil thickness map using an integrated data mining approach,
Geoderma,
Volume 377,
2020,
114579,
ISSN 0016-7061,
https://doi.org/10.1016/j.geoderma.2020.114579.
(https://www.sciencedirect.com/science/article/pii/S0016706119328630)
National Collaborative Research Infrastructure Strategy
All Rights (including copyright) CSIRO 2020.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:61522
2024-01-22T00:01:18Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/603210
https://data.csiro.au/dap/
102.100.100/603210
10.25919/pm2n-ww12
Soil and Landscape Grid National Soil Attribute Maps - Total Nitrogen (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/603210
1950-01-01
2023-12-01
northlimit=-10.7996; southlimit=-43.7676; westlimit=111.1166; eastLimit=154.7446; projection=WGS84
10.25919/pm2n-ww12
Soil and Landscape Grid National Soil Attribute Maps - Total Nitrogen (3" resolution) - Release 2
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Searle
Ross
2024-01-22
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_1121
TERN
TERN_Soils_DSM
TERN_Soils
Soil
Raster
Attribute
Nitrogen
N
total soil nitrogen
soil nitrogen
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
Soil and Landscape Grid of Australia
370401
410604
300206
410601
410602
This is Version 1 of the Australian Total Soil Nitrogen product of the Soil and Landscape Grid of Australia.
The map gives a modelled estimate of the spatial distribution of total nitrogen in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). An additional measure of model reliability is through assessment of model extrapolation risk. This measure provides users a spatial depiction where model estimates are made within the domain of the observed data or not.
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: Total soil nitrogen;
Units: % (percentage of fine soil mass);
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 24;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
A full description of the methods used to generate this product can be found at -https://aussoilsdsm.esoil.io/slga-version-2-products/total-soil-nitrogen
The first effort to derive national digital soil mapping of total soil nitrogen (expressed as a percentage of fine soil mass) is published and available on the CSIRO Data Access Portal among other places. The present work sort to update this mapping as part of ongoing efforts to expand and improve Australia’s national mapping and characterisation of its soil resources. Collectively these national soil mapping efforts constitute the Soil and Landscape Grid of Australia. The original work has been deemed as Version 1 (completed 2015), while the new work logically is Version 2 (completed 2023). This work has been made possible through support and funding from Australia’s National Collaborative Research Infrastructure Strategy (NCRIS) via the Terrestrial and Ecosystem Research Network
As with the first effort, digital soil mapping is the underpinning framework for the ultimate creation of soil maps in this instance.
As with the other more recent national digital soil mapping efforts, the SoilDataFederator (Searle 2020) has been instrumental in the dynamic collation of disparate soil observational datasets from across the country. These data have been sourced mainly from each State and Territory Government departments tasked with soil survey and collection. Plus there are other data contributions from Universities and to a lessor extent individual research groups. The SoilDataFederator also taps into the larger CSIRO developed Natsoil database (CSIRO 2020) which holds the data related to research projects and field stations that CSIRO has managed.
The improvement in digital soil mapping has come about via several mechanism.
1. A huge expansion of the available library of data corresponding to each of the main soil state factors has been made possible (Searle et al. 2022). This is through acquisition of new data sets and improvement of others compared with those used for version 1.
2. Adoption of machine learning to derive empirical relationships between target variable (total soil nitrogen content) and various data related to the state factors that help determine and control soil variability across landscapes, here the Australian continent and very nearshore islands. While the adoption of ML is not an entirely new advancement, the coupling of it with additional data, and integration of it within a psedo-3D predictive framework permit an improved ability to spatially and vertically characterise soils than Version 1 did.
3. Together with a more powerful and streamlined predictive modelling approach, the quantification of uncertainties draws on the use of the UNEEC (Uncertainty Estimation based on Empirical Errors and Clustering; Shrestha and Solomatine 2006) approach instead of bootstrapping approach so that prediction interval bounds are more custom to the variations in state factor information. Bootstrapping tends to create uniform prediction interval ranges, whereas UNEEC can distinguish areas of relatively lower and higher uncertainties based on differences in soil and landscape characteristics. Therefore, for Version 2, the uncertainties are more custom and tightly defined to the environment they are quantified in.
4. An approach to understand and characterise issues of model extrapolation has been developed. This seeks to highlight areas where there is high confidence that models are going be unreliable, because these areas are outside the range of the underpinning data used in modelling. This issue is addressed via combination of data geometric and distance-based techniques.
References:
CSIRO (2020): CSIRO National Soil Site Database. v9. CSIRO. Data Collection. https://doi.org/10.25919/c4br-0r30.
Searle, Ross (2020): TERN Soil Data Federator. v1. CSIRO. Software Collection. http://hdl.handle.net/102.100.100/480151?index=1
https://aussoilsdsm.esoil.io/slga-version-2-products/total-soil-nitrogen
Methods summary
https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
Soil and Landscape Grid of Australia
https://data.csiro.au/categories/kw/TERN_Soils_DSM
Browse all Soil and Landscape Grid of Australia collections
All Rights (including copyright) CSIRO 2023.
Creative Commons Attribution
Data is accessible online and may be reused in accordance with licence conditions
csiro:61526
2024-01-28T22:47:57Z
external_system_set_TERN_SOILS
external_system_set_RDA
102.100.100/608290
https://data.csiro.au/dap/
102.100.100/608290
10.25919/7j78-md43
Soil and Landscape Grid National Soil Attribute Maps - Total Phosphorus (3" resolution) - Release 2
http://hdl.handle.net/102.100.100/608290
1950-01-01
2023-12-01
northlimit=-9.8166; southlimit=-44.0768; westlimit=110.8772; eastLimit=154.6635; projection=WGS84
10.25919/7j78-md43
Soil and Landscape Grid National Soil Attribute Maps - Total Phosphorus (3" resolution) - Release 2
v1
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Malone
Brendan
Searle
Ross
2024-01-29
csiro:R-14062_IRP
csiro:PartyGroup
csiro:service_1123
TERN
TERN_Soils_DSM
TERN_Soils
Soil
Raster
Attribute
Nitrogen
N
total soil nitrogen
soil nitrogen
Continental
DSM
Global Soil Map
Spatial modelling
3-dimensional soil mapping
Spatial uncertainty
Soil Maps
Digital Soil Mapping
SLGA
Soil and Landscape Grid of Australia
410602
370401
410604
410601
300206
This is Version 2 of the Australian Total Soil Phosphorus product of the Soil and Landscape Grid of Australia.
The map gives a modelled estimate of the spatial distribution of total phosphorus in soils across Australia.
The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). An additional measure of model reliability is through assessment of model extrapolation risk. This measure provides users a spatial depiction where model estimates are made within the domain of the observed data or not.
Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html
Attribute Definition: Total soil phosphorus;
Units: % (percentage of fine soil mass);
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 24;
Number of pixels with coverage per layer: 2007M (49200 * 40800);
Data license : Creative Commons Attribution 4.0 (CC BY);
Target data standard: GlobalSoilMap specifications;
Format: Cloud Optimised GeoTIFF;
The first effort to derive national digital soil mapping of total soil phosphorus (expressed as a percentage of fine soil mass) is published and available on the CSIRO Data Access Portal among other places. The present work sort to update this mapping as part of ongoing efforts to expand and improve Australia’s national mapping and characterisation of its soil resources. Collectively these national soil mapping efforts constitute the Soil and Landscape Grid of Australia. The original work has been deemed as Version 1 (completed 2015), while the new work logically is Version 2 (completed 2023). This work has been made possible through support and funding from Australia’s National Collaborative Research Infrastructure Strategy (NCRIS) via the Terrestrial and Ecosystem Research Network
As with the first effort, digital soil mapping is the underpinning framework for the ultimate creation of soil maps in this instance.
As with the other more recent national digital soil mapping efforts, the SoilDataFederator (Searle 2020) has been instrumental in the dynamic collation of disparate soil observational datasets from across the country. These data have been sourced mainly from each State and Territory Government departments tasked with soil survey and collection. Plus there are other data contributions from Universities and to a lessor extent individual research groups. The SoilDataFederator also taps into the larger CSIRO developed Natsoil database (CSIRO 2020) which holds the data related to research projects and field stations that CSIRO has managed.
The improvement in digital soil mapping has come about via several mechanism.
1. A huge expansion of the available library of data corresponding to each of the main soil state factors has been made possible (Searle et al. 2022). This is through acquisition of new data sets and improvement of others compared with those used for version 1.
2. Adoption of machine learning to derive empirical relationships between target variable (total soil phosphorus content) and various data related to the state factors that help determine and control soil variability across landscapes, here the Australian continent and very nearshore islands. While the adoption of ML is not an entirely new advancement, the coupling of it with additional data, and integration of it within a psedo-3D predictive framework permit an improved ability to spatially and vertically characterise soils than Version 1 did.
3. Together with a more powerful and streamlined predictive modelling approach, the quantification of uncertainties draws on the use of the UNEEC (Uncertainty Estimation based on Empirical Errors and Clustering; Shrestha and Solomatine 2006) approach instead of bootstrapping approach so that prediction interval bounds are more custom to the variations in state factor information. Bootstrapping tends to create uniform prediction interval ranges, whereas UNEEC can distinguish areas of relatively lower and higher uncertainties based on differences in soil and landscape characteristics. Therefore, for Version 2, the uncertainties are more custom and tightly defined to the environment they are quantified in.
4. An approach to understand and characterise issues of model extrapolation has been developed. This seeks to highlight areas where there is high confidence that models are going be unreliable, because these areas are outside the range of the underpinning data used in modelling. This issue is addressed via combination of data geometric and distance-based techniques.
CSIRO (2020): CSIRO National Soil Site Database. v9. CSIRO. Data Collection. https://doi.org/10.25919/c4br-0r30.
Searle, Ross (2020): TERN Soil Data Federator. v1. CSIRO. Software Collection. http://hdl.handle.net/102.100.100/480151?index=1
https://aussoilsdsm.esoil.io/slga-version-2-products/total-soil-phosphorus
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https://esoil.io/TERNLandscapes/Public/Pages/SLGA/
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