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: 0-5Â cm, 5-15Â cm, 15-30Â cm, 30-60Â cm, 60-100Â cm and 100-200Â cm. These depths are consistent with the specifications of the GlobalSoilMap.net project. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90Â m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - SLGA.
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: 0-5Â cm, 5-15Â cm, 15-30Â cm, 30-60Â cm, 60-100Â cm and 100-200Â cm. These depths are consistent with the specifications of the GlobalSoilMap.net project. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90Â m pixels).
Detailed information about the Soil and Landscape Grid of Australia can be found at - SLGA.
- Attribute Definition: pH of a 1:5 soil water solution;
- Units: None;
- Period (temporal coverage; approximately): 1950-2021;
- Spatial resolution: 3 arc seconds (approx 90Â 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;
Credit
We 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.
Purpose
The map gives a modelled estimate of the spatial distribution of pH (Water) in soils across Australia.
Lineage
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: 0-5Â cm, 5-15Â cm, 15-30Â cm, 30-60Â cm, 60-100Â cm, 100-200Â cm. 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 ~90Â m 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).
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
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: 0-5Â cm, 5-15Â cm, 15-30Â cm, 30-60Â cm, 60-100Â cm, 100-200Â cm. 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 ~90Â m 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).
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
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