Brief description
This is Version 1 of the Australian Drained Upper Limit Volumetric Water Content (DUL) product of the Soil and Landscape Grid of Australia.The map gives a modelled estimate of the spatial distribution of Drained Upper Limit Volumetric Water Content soil hydraulic property 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. 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: Drained Upper Limit Volumetric Water Content;
- Units: percent;
- 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
A full description of the methods used to generate this product can be found at - https://aussoilsdsm.esoil.io/slga-version-2-products/soil-hydraulic-propertiesWe employed standard Digital Soil Modelling (DSM) (McBratney et. al., 2002) methods utilising publicly available soil observation data and publicly available environmental covariate data in an environmental correlation approach using machine learning to map the soil properties of volumetric (mm/mm) Drained Upper Limit (DUL) and Soil Lower Limit (L15) across the entire continent at 6 standard depths at 90 m pixel resolution.
We used pedotransfer functions for estimating Drained Upper Limit - 1/3 bar (DUL) and Lower Limit - 15 bar (L15) from readily available soil attribute data using data from the National Soil Site Collation (NSSC) (Searle, 2014). Soil property data was obtained using the TERN SoilDataFederator (SDF).
The spatial modelling of DUL and L15 is done at six standard depth intervals conforming to the GlobalSoilMap Specifications. (GlobalSoilMap Science Committee, 2015) ie 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm and 100-200 cm. To facilitate modelling at these standard depths the observed data set depths were harmonised to these depths using a mass preserving spline method as described Bishop (1999). A total of 20545 soil profiles were splined in this way and used as inputs to the spatial modelling.
We utilised the publicly available Terrestrial Ecosystem Research Network (TERN) raster covariate stack. It is comprised of 154 individual raster data layers. (https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/).
The covariate stack was used as the independent variable data for the predictions across all grid cells and at each depth.
The Cubist Machine Learning algorithm (Quinlan, 1992) consisting of 50 bootstrapped model realisations was used to predicted DUL and L15 values (mean of the boostrap realisations) and estimate upper and lower confidence intervals (5% and 95%).
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
Progress Code: completed
Maintenance and Update Frequency: notPlanned
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.
Purpose
The map gives a modelled estimate of the spatial distribution of the Drained Upper Limit Volumetric Water Content in soils across Australia.
The map gives a modelled estimate of the spatial distribution of the Drained Upper Limit Volumetric Water Content in soils across Australia.
Created: 2021-06-21
Issued: 2022-11-11
Modified: 2024-09-27
Data time period: 1950-01-01 to 2021-06-14
Subjects
3-dimensional soil mapping |
30 meters - < 100 meters |
Agricultural and Veterinary Sciences |
AGRICULTURE |
Agriculture, Land and Farm Management |
Agricultural Land Management |
Agricultural Spatial Analysis and Modelling |
DSM |
DUL |
Decadal |
Digital Soil Mapping |
Drained Upper Limit Volumetric Water Content |
EARTH SCIENCE |
Environmental Sciences |
Global Soil Map |
LAND SURFACE |
Percent |
Raster |
SLGA |
Soil Sciences |
SOILS |
Soil |
Soil Hydraulic Properties |
Soil Maps |
Soil Sciences Not Elsewhere Classified |
Spatial modelling |
Spatial uncertainty |
environment |
geoscientificInformation |
soil volumetric water content (Percent) |
User Contributed Tags
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Other Information
Point-of-truth metadata URL
Methods Summary - Soil Hydraulic Properties
uri :
https://aussoilsdsm.esoil.io/slga-version-2-products/soil-hydraulic-properties
McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.
doi :
https://doi.org/10.1016/S0016-7061(03)00223-4
Specifications for GlobalSoilMap products.
uri :
https://www.isric.org/sites/default/files/GlobalSoilMap_specifications_december_2015_2.pdf
Quinlan, J.R. (1992). Learning With Continuous Classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, pp. 343-348.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Identifiers
- global : de9ddc12-b8e4-4ff2-99c4-390227a848aa
- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/de9ddc12-b8e4-4ff2-99c4-390227a848aa
