This is Version 2 of the Australian soil pH (CaCl2) product of the Soil and Landscape Grid of Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F17EC6AB6E
The map gives a modelled estimate of the spatial distribution of the pH of 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). An additional measure of model reliability is through assessment of model extrapolation risk. This measure provides users a spatial depiction where model estimates are made within the domain of the observed data or not.
Detailed information about the Soil and Landscape Grid of Australia can be found at - SLGA
- Total number of gridded maps for this attribute: 24.
- Number of pixels with coverage per layer: 2007M (49200 * 40800).
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F17EC6AB6E
The map gives a modelled estimate of the spatial distribution of the pH of 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). An additional measure of model reliability is through assessment of model extrapolation risk. This measure provides users a spatial depiction where model estimates are made within the domain of the observed data or not.
Detailed information about the Soil and Landscape Grid of Australia can be found at - SLGA
- Total number of gridded maps for this attribute: 24.
- Number of pixels with coverage per layer: 2007M (49200 * 40800).
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 the pH of soils across Australia.
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.
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-cacl2
Release 2 has come about via several mechanism and presents a completely different approach as to how release 1 was developed. Namely:
1. A huge expansion of the available library of data corresponding to each of the main soil state factors has been made possible (Searle et al. 2022). This is through acquisition of new data sets and improvement of others compared with those used for version 1.
2. Adoption of machine learning to derive empirical relationships between target variable (total soil nitrogen content) and various data related to the state factors that help determine and control soil variability across landscapes, here the Australian continent and very nearshore islands. While the adoption of ML is not an entirely new advancement, the coupling of it with additional data, and integration of it within a psedo-3D predictive framework permit an improved ability to spatially and vertically characterise soils than Version 1 did.
3. Together with a more powerful and streamlined predictive modelling approach, the quantification of uncertainties draws on the use of the UNEEC (Uncertainty Estimation based on Empirical Errors and Clustering; Shrestha and Solomatine 2006) approach instead of bootstrapping approach so that prediction interval bounds are more custom to the variations in state factor information. Bootstrapping tends to create uniform prediction interval ranges, whereas UNEEC can distinguish areas of relatively lower and higher uncertainties based on differences in soil and landscape characteristics. Therefore, for Version 2, the uncertainties are more custom and tightly defined to the environment they are quantified in.
4. An approach to understand and characterise issues of model extrapolation has been developed. This seeks to highlight areas where there is high confidence that models are going be unreliable, because these areas are outside the range of the underpinning data used in modelling. This issue is addressed via combination of data geometric and distance-based techniques.
The sequence of steps below were carried out to develop the Version 2 products:
- Prepared point and covariate data, including filtering, cleansing, and harmonisation.
- Point data intersection with covariates.
- Creation of model and test data sets.
- Ranger model hyperparameter value optimisation.
- Ranger model fitting with best hyperparameters.
- Spatialisation of ranger models.
- Uncertainty analysis with UNEEC method including rudimentary optimisation of class number size.
- Spatialisation of model uncertainties.
- Model extrapolation work with count of observation and boundary method (point data).
- Ranger model fitting of extrapolation outcomes.
- Spatialisation of model extrapolation outcomes.
- Model evaluations with both test data and against SLGA Version 1 products.
- Delivery of digital soil mapping outputs and computer code to repository.
Release 2 has come about via several mechanism and presents a completely different approach as to how release 1 was developed. Namely:
1. A huge expansion of the available library of data corresponding to each of the main soil state factors has been made possible (Searle et al. 2022). This is through acquisition of new data sets and improvement of others compared with those used for version 1.
2. Adoption of machine learning to derive empirical relationships between target variable (total soil nitrogen content) and various data related to the state factors that help determine and control soil variability across landscapes, here the Australian continent and very nearshore islands. While the adoption of ML is not an entirely new advancement, the coupling of it with additional data, and integration of it within a psedo-3D predictive framework permit an improved ability to spatially and vertically characterise soils than Version 1 did.
3. Together with a more powerful and streamlined predictive modelling approach, the quantification of uncertainties draws on the use of the UNEEC (Uncertainty Estimation based on Empirical Errors and Clustering; Shrestha and Solomatine 2006) approach instead of bootstrapping approach so that prediction interval bounds are more custom to the variations in state factor information. Bootstrapping tends to create uniform prediction interval ranges, whereas UNEEC can distinguish areas of relatively lower and higher uncertainties based on differences in soil and landscape characteristics. Therefore, for Version 2, the uncertainties are more custom and tightly defined to the environment they are quantified in.
4. An approach to understand and characterise issues of model extrapolation has been developed. This seeks to highlight areas where there is high confidence that models are going be unreliable, because these areas are outside the range of the underpinning data used in modelling. This issue is addressed via combination of data geometric and distance-based techniques.
The sequence of steps below were carried out to develop the Version 2 products:
- Prepared point and covariate data, including filtering, cleansing, and harmonisation.
- Point data intersection with covariates.
- Creation of model and test data sets.
- Ranger model hyperparameter value optimisation.
- Ranger model fitting with best hyperparameters.
- Spatialisation of ranger models.
- Uncertainty analysis with UNEEC method including rudimentary optimisation of class number size.
- Spatialisation of model uncertainties.
- Model extrapolation work with count of observation and boundary method (point data).
- Ranger model fitting of extrapolation outcomes.
- Spatialisation of model extrapolation outcomes.
- Model evaluations with both test data and against SLGA Version 1 products.
- Delivery of digital soil mapping outputs and computer code to repository.
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