Soil and Landscape Grid National Soil Attribute Maps - pH - CaCl2 (3" resolution) - Release 2

Brief description

This is Version 2 of the Australian soil pH (CaCl2) product of the Soil and Landscape Grid of Australia.

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: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. 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).

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 - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: soil pH (CaCl2)
Units: pH units;
Period (temporal coverage; approximately): 1950-2021;
Spatial resolution: 3 arc seconds (approx 90m);
Total number of gridded maps for this attribute: 24;
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: 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. The incorporation of soil pH data measured using field method (Raupach's Indicator test method) into the modelling system. An empirical transfer function was developed based on measurements with both lab and field observations (52629) to extend to measures where only field data was available. Combining lab and field measures required a special model fitting to account for differing magnitudes of error in the pH data. Lab data was assumed to be error free, however pH and estimated uncertainty could be estimated by the empirical transfer function, then incorporated into the spatial modelling system.

2. Adoption of machine learning to derive empirical relationships between target variable (soil pH) 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.

2. 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.

2. 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

- Data extraction from SoilDataFederator

- Development of transfer function using data cases with corresponding field and lab information.

- Integration of lab and field data whereby estimates of pH 4B1 from field data are propagated from empirical distributions in order for uncertainty of data is sufficiently handled in later spatial modelling steps.

- Point data intersection with covariates.

- Creation of model and test data sets. Test cases were extracted from datasets for each depth interval.

- Ranger model hyperparameter value optimisation

- Variogram model fitting of ranger model residuals.

- Spatialisation of ranger models and residual kriging models

- Uncertainty analysis with UNEEC method including rudimentary optimisation of class number size.

- Spatialisation of model uncertainties.

- Model evaluations with both test data and against SLGA Version 1 products.

- Delivery of digital soil mapping outputs and computer code to repository.