Brief description
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: 0-5 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 - GlobalSoilMaps.
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 - SLGA
- 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 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;
- Format: Cloud Optimised GeoTIFF.
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 aim is to operate an open national capability that provides access to verified, science-quality land surface dynamics data and soils information layers, plus high-end data analytics tools that integrated with other TERN observations can meet the needs of ecosystem researchers and actionable information for policy makers and natural resource managers.
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.
Updating the Australian digital soil texture mapping (Part 2*): spatial modelling of merged field and lab measurements
uri :
https://www.publish.csiro.au/sr/Fulltext/SR20284
Created: 1950-01-01
Issued: 2022-10-28
Modified: 2024-09-23
Data time period: 1950-01-01 to 2021-09-13
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Point-of-truth metadata URL
Methods Summary for Soil Texture
uri :
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-texture
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
uri :
https://www.r-project.org/
Searle, R., Malone, B., Wilford, J., Austin, J., Ware, C., Webb, M., Roman Dobarco, M., Van Niel, T., (2022). TERN Digital Soil Mapping Raster Covariate Stacks. v2. CSIRO. Data Collection. https://doi.org/10.25919/jr32-yq58
doi :
https://doi.org/10.25919/jr32-yq58
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 - Clay (3" resolution) - Release 1. CSIRO. Data Collection. https://doi.org/10.4225/08/546EEE35164BF
doi :
https://doi.org/10.4225/08/546EEE35164BF
Viscarra Rossel R. A., Chen C., Grundy M. J., Searle R., Clifford D., Campbell P. H. (2015) The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research 53, 845-864. https://doi.org/10.1071/SR14366
- global : f95dc442-013b-4fad-b31f-91ba86fbe7f5
- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/f95dc442-013b-4fad-b31f-91ba86fbe7f5
