Data

Soil and Landscape Grid National Soil Attribute Maps - 15 Bar Lower Limit Volumetric Water Content (Percent) (3 arc second resolution) Version 1

Terrestrial Ecosystem Research Network
Searle, Ross ; Nimalka Somarathna, P. D. Sanjeewani
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/awp8-nv68&rft.title=Soil and Landscape Grid National Soil Attribute Maps - 15 Bar Lower Limit Volumetric Water Content (Percent) (3 arc second resolution) Version 1&rft.identifier=10.25919/awp8-nv68&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=This is Version 1 of the Australian 15 Bar Lower Limit Volumetric Water Content (L15) product of the Soil and Landscape Grid of Australia. The map gives a modelled estimate of the spatial distribution of 15 Bar Lower Limit Volumetric Water Content 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 - SLGA. Attribute Definition: 15 Bar Lower 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;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-properties We 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) (https://aussoilsdsm.esoil.io/site-data/soildatafederator). 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/Pages/SLGA/GetData-COGSDataStore_90m_Covariates.html). 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 bootstrap 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.htmlProgress Code: completedMaintenance and Update Frequency: notPlanned&rft.creator=Searle, Ross &rft.creator=Nimalka Somarathna, P. D. Sanjeewani &rft.date=2022&rft.edition=1.0&rft.coverage=northlimit=-10.000416666; southlimit=-44.000416667; westlimit=112.999583333; eastLimit=153.999583334; projection=EPSG:4326&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_rights=&rft_rights=TERN services are provided on an as-is and as available basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.<br> Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.<br><br> Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting<br> Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.&rft_subject=environment&rft_subject=geoscientificInformation&rft_subject=SOILS&rft_subject=Agricultural Land Management&rft_subject=AGRICULTURAL AND VETERINARY SCIENCES&rft_subject=AGRICULTURE, LAND AND FARM MANAGEMENT&rft_subject=Agricultural Spatial Analysis and Modelling&rft_subject=SOIL SCIENCES&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Soil Sciences not elsewhere classified&rft_subject=soil volumetric water content (Percent)&rft_subject=Percent&rft_subject=30 meters - < 100 meters&rft_subject=Decadal&rft_subject=Soil&rft_subject=Raster&rft_subject=Soil Hydraulic Properties&rft_subject=15 Bar&rft_subject=Lower Limit Volumetric Water Content&rft_subject=DUL&rft_subject=DSM&rft_subject=Global Soil Map&rft_subject=Spatial modelling&rft_subject=3-dimensional soil mapping&rft_subject=Spatial uncertainty&rft_subject=Soil Maps&rft_subject=Digital Soil Mapping&rft_subject=SLGA&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
CC-BY

Creative Commons Attribution 4.0 International Licence
http://creativecommons.org/licenses/by/4.0

TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.

Access:

Open view details

unclassified

Contact Information

Street Address:
Terrestrial Ecosystem Research Network
Building 1019, 80 Meiers Rd
QLD 4068
Australia
Ph: +61 7 3365 9097

esupport@tern.org.au

Brief description

This is Version 1 of the Australian 15 Bar Lower Limit Volumetric Water Content (L15) product of the Soil and Landscape Grid of Australia.

The map gives a modelled estimate of the spatial distribution of 15 Bar Lower Limit Volumetric Water Content 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 - SLGA.

  • Attribute Definition: 15 Bar Lower 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-properties

We 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) (https://aussoilsdsm.esoil.io/site-data/soildatafederator).

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/Pages/SLGA/GetData-COGSDataStore_90m_Covariates.html).

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 bootstrap 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

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 15 Bar Lower Limit Volumetric Water Content in soils across Australia.

Created: 2021-06-21

Issued: 2022-11-10

Modified: 2024-09-27

Data time period: 1950-01-01 to 2021-06-14

This dataset is part of a larger collection

153.99958,-10.00042 153.99958,-44.00042 112.99958,-44.00042 112.99958,-10.00042 153.99958,-10.00042

133.4995833335,-27.0004166665

Other Information
Point-of-truth metadata URL

uri : https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/4443f5df-a0b2-4352-b44b-83f7feb1e27d

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. https://doi.org/10.1016/S0016-7061(03)00223-4

doi : https://doi.org/10.1016/S0016-7061(03)00223-4

Global Soil Map Specifications

uri : https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf

Searle, R. (2014). The Australian site data collation to support the GlobalSoilMap. GlobalSoilMap: Basis of the global spatial soil information system, 127.

uri : https://books.google.com/books?hl=en&lr=&id=S5ClAgAAQBAJ&oi=fnd&pg=PA127&ots=ipkrum6Ixj&sig=MKuhFzq8TBkgCV14NT5ChcWVeg4

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

uri : https://www.R-project.org/