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Soil and Landscape Grid Australia-Wide 3D Soil Property Maps (3" resolution) - Release 1

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About this Collection

Soil and Landscape Grid Australia-Wide 3D Soil Property Maps (3" resolution) - Release 1


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.ne... more


Soil Sciences not elsewhere classified


https://doi.org/10.4225/08/5aaf553b63215


01 Jan 1950


31 Dec 2013


CSIRO Enquiries
CSIROEnquiries@csiro.au
1300 363 400

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 ... more


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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).


We thank the CSIRO, Terrestrial Ecosystem Research Network (TERN), the Australian Government through the National Collaborative Research Infrastructure Strategy and the Super Science Initiative, for funding the project. We are grateful to the custodians of the soil site data in each state and territory for providing access to the soil site data, and all of the organisations listed as collaborating agencies for their significant contributions to the project and its outcomes. We thank also D. Jacquier and P. Wilson for their inputs.


Creative Commons Attribution 4.0 International Licence


CSIRO (Australia), Geoscience Australia (Australia), Northern Territory Department of Land Resource Management (Australia), NSW Office of Environment and Heritage (Australia), Queensland Department of Science, Information Technology, Innovation and the Arts (DSITIA) (Australia), South Australia Department of Environment, Water and Natural Resources (Australia), Tasmania Department Primary Industries, Parks, Water and Environment (Australia), University of Sydney (Australia), Victoria Department of Environment and Primary Industries (Australia), Western Australia Department of Agriculture and Food (Australia)


Viscarra Rossel, Raphael; Chen, Charlie; Grundy, Mike; Searle, Ross; Clifford, David (2014): Soil and Landscape Grid Australia-Wide 3D Soil Property Maps (3" resolution) - Release 1. v3. CSIRO. Data Collection. https://doi.org/10.4225/08/5aaf553b63215


All Rights (including copyright) CSIRO 2014.


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Location Details

9°59′53.9153″ S


43°38′32.9105″ S


153°38′23.8788″ E


112°54′44.8847″ E


WGS84


More about this Collection

Peter Wilson


Research Scientist


-2 m


0 m



Raster




eng


UTF8


Environment


About this Project

1181.2 TERN Facility No9 InfoGrid GRUNDY


The Soil and Landscape Grid of Australia is a comprehensive fine spatial resolution grid of functional soil attributes and key landscape features across Australia. The landscape attributes are derived from the data collected by the Shuttle Radar Topography Mission, whilst the soil attribute surfaces are modelled from existing soils information. The... more


Mike Grundy


National Soil Grid


The Soil and Landscape Grid of Australia Facility has produced a comprehensive fine-resolution grid of soil attributes and important land surface parameters. The data is consistent with the Specifications of the GlobalSoilMap and is managed as part of the Australian Soil Resource Information System (ASRIS). There are a range of soil attribute produ... more


Modelling


Raphael Viscarra Rossel


Charlie Chen


Mike Grundy


Ross Searle


David Clifford


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