Brief description
This is Version 2 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546EE212B0048
The map gives a modelled estimate of the spatial distribution of Bulk Density 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: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method;
- Units: g/cm3;
- 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
An attempt was made to update digital soil mapping of whole soil bulk density for Australia. This was an update of first attempt by Viscarra Rossel et al. (2014). Based on model evaluations using a dataset not included in any modelling, the updated version (2nd Version) represents a demonstrable improvement on the 1st version.Since the first version, more measured site data has been made available and retrievable via the Australian SoilDataFederator. In 2014 there were 3776 sites with measured whole soil bulk density. For the new update, 6116 sites had measured data. Because of usually strong empirical relationships between bulk density, soil texture and soil carbon, the use of pedotransfer functions (to predict bulk density from soil texture and soil carbon) was performed with the intention of increasing data density and spatial coverage of data that would ultimately improve digital soil mapping prediction skill. This added a further 15735 sites after building a spatial pedotransfer function using a dataset of 12308 cases (3939 sites with bulk density, soil carbon and soil texture data).
The basic steps of the work entailed:
- Use soil data federator to get pertinent soils observation data.
- Develop spatial pedotransfer function prediction whole soil bulk density using soil carbon and texture data.
- Compile measured and inferred whole soil bulk density data (86306 cases), then setting aside a dataset of 7500 cases for external model evaluation.
- Predictive models using random forest algorithm with 78806 data cases fitted. To account for uncertainties in pedotransfer function inferred data, Monte Carlo simulations were performed from the pedotransfer function model. Simulation was repeated 100 times.
- Predictive model uncertainties quantified using UNEEC approach (Uncertainty Estimation based on local errors and Clustering).
- Quantification of model extension limits derived using hybrid method involving multivariate convex hull analysis and count of observations.
- Digital soil maps with quantified uncertainties (5th and 95th prediction interval limits) and assessment of model extrapolation risk were produced at 90 m resolution for the following depths: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, 100-200 cm.
- 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
All processing for the generation of these products was undertaken using the R programming language (R Core Team, 2020).
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 map gives a modelled estimate of the spatial distribution of Bulk Density in 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.
Created: 1950-01-01
Issued: 2023-06-19
Modified: 2024-08-09
Data time period: 1950-01-01 to 2023-06-01
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Point-of-truth metadata URL
Malone, B. P., Minansy, B., & Brungard, C. (2019). Some methods to improve the utility of conditioned Latin hypercube sampling. PeerJ, 7, e6451. https://doi.org/10.7717/peerj.6451
doi :
https://doi.org/10.7717/peerj.6451
Methods Summary for modelling of Whole Soil Bulk Density
uri :
https://aussoilsdsm.esoil.io/slga-version-2-products/whole-soil-bulk-density
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
van den Hoogen, J., Robmann, N., Routh, D., Lauber, T., van Tiel, N., Danylo, O., & Crowther, T.W., (2021). A geospatial mapping pipeline for ecologists. bioRxiv 2021.07.07.451145
doi :
https://doi.org/10.1101/2021.07.07.451145
Viscarra Rossel, R., Chen, C., Grundy, M., Searle, R., Clifford, D., Odgers, N., Holmes, K., Griffin, T., Liddicoat, C., & Kidd, D.,(2014). Soil and Landscape Grid National Soil Attribute Maps - Bulk Density - Whole Earth (3" resolution) - Release 1. v6. CSIRO. Data Collection. https://doi.org/10.4225/08/546EE212B0048
- global : 95978aec-6ba8-446b-a721-2b875d9f61a8
- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/95978aec-6ba8-446b-a721-2b875d9f61a8
