Brief description
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: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm and 100-200 cm. 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 90 m);
- 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;
Lineage
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).
- 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/SLGA/GetData-COGSDataStore.html
Notes
CreditWe at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
This work was jointly funded by CSIRO, Terrestrial Ecosystem Research Network (TERN) and the Australian Government through the National Collaborative Research Infrastructure Strategy (NCRIS).
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.
The map gives a modelled estimate of the spatial distribution of sand in soils across Australia.
Data Quality Assessment Scope
local :
dataset
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.
Malone, B., & Searle, R. (2021). Updating the Australian digital soil texture mapping (Part 2*): spatial modelling of merged field and lab measurements. Soil Research, 59(5), 435–451.
doi :
https://doi.org/10.1071/SR20284
Created: 2021-09-13
Issued: 2022-10-28
Modified: 2024-09-27
Data time period: 1950-01-01 to 2021-09-13
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Point-of-truth metadata URL
Soil Texture Mapping V2 Method Summary
uri :
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-texture
Malone, B., & Searle, R. (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(5), 419–434.
doi :
https://doi.org/10.1071/SR20283
Malone, B., & Searle, R. (2021). Updating the Australian digital soil texture mapping (Part 2*): spatial modelling of merged field and lab measurements. Soil Research, 59(5), 435–451. https://doi.org/10.1071/SR20284
doi :
https://doi.org/10.1071/SR20284
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
uri :
https://www.R-project.org/.
Viscarra Rossel, R., Chen, C., Grundy, M., Searle, R., Odgers, N., Holmes, K., Griffin, T., Liddicoat, C., Kidd, D., & Clifford, D., (2014). Soil and Landscape Grid National Soil Attribute Maps - Sand (3" resolution) - Release 1. CSIRO. Data Collection.
- global : 4224ddff-5fb4-4170-b5ea-c0c500599700
- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/4224ddff-5fb4-4170-b5ea-c0c500599700
