Showing results for: [ Soil visible-near infrared spectra ]
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... more 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.less
CLSD TERN Facility No 9 Info Grid - National Soil Grid - Published 28 Mar 2018
We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer. These spectra provide an integrative measure that provides information on the fundamental chara... morecteristics and composition of the soil, including colour, iron oxide, clay and carbonate mineralogy, organic matter content and composition, the amount of water present and particle size. This soil information content of the spectra was summarised using a principal component analysis (PCA). We used model trees to derive statistical relationships between the scores of the PCA and 31 predictors that were readily available and we thought might best represent the factors of soil formation (climate, organisms, relief, parent material, time and the soil itself). The models were validated and subsequently used to produce digital maps of the information content of the spectra, as summarised by the PCA, with estimates of prediction error at 3-arc seconds (around 90 m) pixel resolution. The maps might be useful in situations requiring high-resolution, quantitative soil information e.g. in agricultural, environmental and ecologic modelling and for soil mapping and classification.
Units of measurement:
1. Principal component 1;
2. Principal component 3;
3. Principal component 3.
For interpretations please see Viscarra Rossel & Chen (2011).
Data Type: Float Grid.
Map Projection: Geographic.
Map units: Decimal degrees.
Resolution: 0.00083333333 degrees.
File Header Information:
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CLSD DAFF ACLEP DSM Activity 2 - ACLEP digital soil mapping - Published 28 Aug 2015