Data

Soil and Landscape Grid National Soil Attribute Maps - Cation Exchange Capacity (3" resolution) - Release 1

Terrestrial Ecosystem Research Network
Malone, Brendan
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/pkva-gf85&rft.title=Soil and Landscape Grid National Soil Attribute Maps - Cation Exchange Capacity (3 resolution) - Release 1&rft.identifier=10.25919/pkva-gf85&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=This is Version 1 of the Australian Soil Cation Exchange Capacity product of the Soil and Landscape Grid of Australia. The map gives a modelled estimate of the spatial distribution of cation exchange capacity 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: Cation Exchange Capacity (Units: meq/100g); Period (temporal coverage; approximately): 1970-2022; 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); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF.Version 1 Soil and landscape Grid of Australia (Grundy et al. 2015), produced digital mapping of Effective Cation Exchange Capacity (ECEC) which is defined as the total amount of exchangeable bases which are mostly sodium, potassium, calcium and magnesium (collectively termed as bases) in non-acidic soils and bases plus aluminium and hydrogen in acidic soils. This product, Soil and Landscape Grid National Soil Attribute Maps - Cation Exchange Capacity, described here entails the use of those data pertaining to those data with CEC measurement. This dataset is made of soil measurements using the following methods as described in Rayment and Lyons (2010): method not recorded (1096), 15A1 (161), 15A2 (365), 15B1 (553), 15B2 (34), 15C1 (3229), 15D1 (265), 15E1 (28), 15K1 (376). The distribution of these sites, colour-coded by each method is shown on Figure 1. See, AusSoilsDSM for details. To complement the CEC measurement data, we used data cases (12474) where there is a measured CEC together with soil texture and soil organic carbon co-located measurements. A machine learning pedotransfer function model with these data, together with spatial covariates was used to extend the geographic spread and density of CEC data in order to potentially improve digital soil mapping efforts. Extensive data processing was involved post data extraction from the Soil Data Federator managed by CSIRO. Spatial modelling is underpinned by the Cubist (Quinlan 1993) machine learning algorithm. The spatial modelling integrates both measurement CEC data and CEC data derived by pedotransfer function. The derived CEC have an associated uncertainty and this is incorporated into the spatial model via a simple monte-carlo approach. The spatial model included a soil depth interval term in order to exploit covariance relationships of soil information within a soil profile. Thus modelling is considered a full soil profile predictive modelling framework. Prediction uncertainties in this work were done using an approach based on local-errors and clustering (UNEEC) method developed by Shrestha and Solomatine (2006). Soil maps of predictions and associated uncertainties (expressed as lower and upper prediction limits for 90% confidence) were generated for the following depth intervals: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, 100-200 cm. 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=Malone, Brendan &rft.date=2023&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 /><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 /><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=AGRICULTURAL LANDS&rft_subject=EARTH SCIENCE&rft_subject=BIOSPHERE&rft_subject=TERRESTRIAL ECOSYSTEMS&rft_subject=CATION EXCHANGE CAPACITY&rft_subject=LAND SURFACE MODELS&rft_subject=EARTH SCIENCE SERVICES&rft_subject=MODELS&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 cation exchange capacity (CEC) (Centimole per kilogram)&rft_subject=Centimole per kilogram&rft_subject=30 meters - < 100 meters&rft_subject=Decadal&rft_subject=TERN_Soils&rft_subject=TERN_Soils_DSM&rft_subject=Raster&rft_subject=Continental&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

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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}.

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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 Soil Cation Exchange Capacity product of the Soil and Landscape Grid of Australia.

The map gives a modelled estimate of the spatial distribution of cation exchange capacity 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: Cation Exchange Capacity (Units: meq/100g);
  • Period (temporal coverage; approximately): 1970-2022;
  • 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);
  • Target data standard: GlobalSoilMap specifications;
  • Format: Cloud Optimised GeoTIFF.

Lineage

Version 1 Soil and landscape Grid of Australia (Grundy et al. 2015), produced digital mapping of Effective Cation Exchange Capacity (ECEC) which is defined as the total amount of exchangeable bases which are mostly sodium, potassium, calcium and magnesium (collectively termed as bases) in non-acidic soils and bases plus aluminium and hydrogen in acidic soils.

This product, Soil and Landscape Grid National Soil Attribute Maps - Cation Exchange Capacity, described here entails the use of those data pertaining to those data with CEC measurement.

This dataset is made of soil measurements using the following methods as described in Rayment and Lyons (2010): method not recorded (1096), 15A1 (161), 15A2 (365), 15B1 (553), 15B2 (34), 15C1 (3229), 15D1 (265), 15E1 (28), 15K1 (376). The distribution of these sites, colour-coded by each method is shown on Figure 1. See, AusSoilsDSM for details.

To complement the CEC measurement data, we used data cases (12474) where there is a measured CEC together with soil texture and soil organic carbon co-located measurements. A machine learning pedotransfer function model with these data, together with spatial covariates was used to extend the geographic spread and density of CEC data in order to potentially improve digital soil mapping efforts.

Extensive data processing was involved post data extraction from the Soil Data Federator managed by CSIRO.

Spatial modelling is underpinned by the Cubist (Quinlan 1993) machine learning algorithm.

The spatial modelling integrates both measurement CEC data and CEC data derived by pedotransfer function. The derived CEC have an associated uncertainty and this is incorporated into the spatial model via a simple monte-carlo approach.

The spatial model included a soil depth interval term in order to exploit covariance relationships of soil information within a soil profile. Thus modelling is considered a full soil profile predictive modelling framework.

Prediction uncertainties in this work were done using an approach based on local-errors and clustering (UNEEC) method developed by Shrestha and Solomatine (2006).

Soil maps of predictions and associated uncertainties (expressed as lower and upper prediction limits for 90% confidence) were generated for the following depth intervals: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, 100-200 cm.

All processing for the generation of these products was undertaken using the R programming language (R Core Team, 2020).

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.


The observed data used to produce this map was obtained from state and federal soil survey agencies. The work was supported by TERN. CSIRO maintains and makes the data available through the Australian Soil Resource Information System.
Purpose
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: 2022-07-27

Issued: 2023-11-20

Modified: 2014-07-14

Data time period: to 2022-07-27

This dataset is part of a larger collection

Click to explore relationships graph

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/5b4b2991-bfa6-41df-be33-7009a5d0a5b0

Soil Cation Exchange Capacity

uri : https://aussoilsdsm.esoil.io/slga-version-2-products/cation-exchange-capacity

Grundy, M.J., Rossel, R.A.V., Searle, R.D., Wilson, P.L., Chen, C., Gregory, L.J., 2015. Soil and Landscape Grid of Australia. Soil Research 53(8), 835-844.

doi : https://doi.org/10.1071/SR15191

Quinlan, J., 1993. Combining Instance-Based and Model-Based Learning. In: Machine Learning Proceedings 1993. Elsevier.

doi : https://doi.org/10.1016/b978-1-55860-307-3.50037-x

Shrestha, D.L., Solomatine, D.P., 2006. Machine learning approaches for estimation of prediction interval for the model output. Neural Networks 19(2), 225-235.

doi : https://doi.org/10.1016/j.neunet.2006.01.012