TERN Geospatial Catalogue
Soil and Landscape Grid National Soil Attribute Maps - Organic Carbon (3" resolution) - Release 2
This is Version 2 of the Australian Soil Organic Carbon product of the Soil and Landscape Grid of Australia.
The map gives a modelled estimate of the spatial distribution of total organic carbon in soils across Australia.
It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/547523BB0801A
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).
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: Mass fraction of carbon by weight in the < 2 mm soil material as determined by dry combustion at 900 Celsius
Units: %;
Period (temporal coverage; approximately): 1970-2021;
Spatial resolution: 3 arc seconds (approx 90m);
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
Simple
Identification info
- Date (Creation)
- 2022-07-27
- Date (Publication)
- 2022-12-19
- Date (Revision)
- 2014-07-14
- Edition
- 2.0
Identifier
Publisher
Author
Sydney Institute of Agriculture, University of Sydney - Wadoux, Alexandre ()
380 Werombi Road, Camden, NSW, 2570, Australia
Camden
NSW
2570
Australia
Co-author
Sydney Institute of Agriculture, University of Sydney - Dobarco, Mercedes ()
380 Werombi Road, Camden, NSW, 2570, Australia
Camden
NSW
2570
Australia
Co-author
CSIRO Land and Water - Malone, Brendan ()
Building 101, Clunies Ross St, Black Mountain, ACT, 2601, Australia
Black Mountain
ACT
2601
Australia
Co-author
Sydney Institute of Agriculture, University of Sydney - Minasny, Budiman ()
380 Werombi Road, Camden, NSW, 2570, Australia
Camden
NSW
2570
Australia
Co-author
Sydney Institute of Agriculture, University of Sydney - McBratney, Alex (Director - Sydney Institute of Agriculture, Professor of Digital Agriculture & Soil Science)
380 Werombi Road, Camden, NSW, 2570, Australia
Camden
NSW
2570
Australia
Co-author
CSIRO Agriculture and Food - Searle, Ross (Senior Experimental Scientist)
306 Carmody Road, St Lucia, Queensland, 4067, Australia
St Lucia
Queensland
4067
Australia
- Website
- https://www.tern.org.au/
- Purpose
- The map gives a modelled estimate of the spatial distribution of total organic carbon in soils across Australia.
- 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.
- Credit
- 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 through the Australian Soil Resource Information System.
- Status
- Completed
Point of contact
Sydney Institute of Agriculture, University of Sydney - Wadoux, Alexandre ()
380 Werombi Road, Camden, NSW, 2570, Australia
380 Werombi Road
Camden
NSW
2570
Australia
Spatial resolution
- Spatial resolution
- 90
- Topic category
-
- Environment
- Geoscientific information
Extent
N
S
E
W
))
Temporal extent
- Time period
- 1970-01-01 2022-07-27
- Title
- Improvements to the Australian national soil thickness map using an integrated data mining approach (2020)
- Website
-
Improvements to the Australian national soil thickness map using an integrated data mining approach (2020)
Related documentation
- Maintenance and update frequency
- Not planned
- GCMD Science Keywords
- ANZSRC Fields of Research
- TERN Parameter Vocabulary
- GCMD Horizontal Resolution Ranges
- GCMD Temporal Resolution Ranges
Resource constraints
- Use limitation
- The Creative Commons Attribution 4.0 International (CC BY 4.0) license allows others to copy, distribute, display, and create derivative works provided that they credit the original source and any other nominated parties. Details are provided at https://creativecommons.org/licenses/by/4.0/
- File name
- 88x31.png
- File description
- CCBy Logo from creativecommons.org
- File type
- png
- Title
- Creative Commons Attribution 4.0 International Licence
- Alternate title
- CC-BY
- Edition
- 4.0
- Access constraints
- License
- Use constraints
- Other restrictions
- Other constraints
- 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
Resource constraints
- Classification
- Unclassified
- Environment description
- All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 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
Distribution Information
Distributor
Distributor
Terrestrial Ecosystem Research Network
Building 1019, 80 Meiers Rd, Indooroopilly, QLD, Australia, 4068
Indooroopilly
QLD
4068
Australia
- OnLine resource
- TERN DataStore
Data quality info
- Hierarchy level
- Dataset
- Abstract
- PlaceHolder Text = Name of the report
Resource lineage
- Statement
- Data on total organic carbon (TOC) concentration (%) was extracted with the Soil Data Federator - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html managed by CSIRO. The Soil Data Federator is a web API that compiles soil data from various institutions and government agencies throughout Australia. The laboratory methods for total organic carbon included in the study are 6A1, 6A1_UC, 6B2, 6B2b, 6B3, 6B3a. We selected TOC data from the period 1970-2020 to get a compromise between representativity of current TOC concentration and spatial coverage. The data was cleaned and processed to harmonize units, exclude duplicates and potentially wrong data entries (e.g. missing upper or lower horizon depths, extreme TOC values, unknown sampling date). Additional TOC measurements from the Biome of Australian Soil Environments (BASE) contextual data (Bisset et al., 2016) were also included in the analyses. TOC concentration for BASE samples was determined by the Walkley-Black method (method 6A1). Upper limits for TOC concentration by biome and land cover classes were set according to published literature, consistent datasets (Australian national Soil Carbon Research Program (SCaRP) and BASE, and data exploration to exclude unrealistic TOC values (e.g. maximum TOC = 30% in temperate forests, maximum TOC = 14% in temperate rainfed pasture). Since TOC concentration in Australian ecosystems has been underestimated by previous SOC maps, we did not set conservative TOC upper limits, knowing that machine learning model would likely underestimate high SOC values. The equal-area quadratic spline function were fitted to the whole collection of pre-processed TOC data, and then values extracted for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm depth intervals, following GlobalSoilMap specifications (Arrouays et al., 2014}. Boxplots with TOC values by biome and land cover after data cleaning and depth standardization are shown in Figure 1. Covariates: We collected a set of 57 spatially exhaustive environmental covariates covering Australia and representing proxies for factors influencing SOC formation and spatial distribution: soil properties, climate, organisms/vegetation, relief and parent material/age. The covariates were reprojected to WGS84 (EPSG:4326) projection and cropped to the same spatial extent. All covariates were resampled using bilinear interpolation or aggregated to conform with a spatial resolution with grid cell of 90 m x 90 m. Mapping: The spatial distribution of soil TOC concentration is driven by the combined influence of climate, vegetation, relief and parent materials. We thus modelled TOC concentration as a function of environmental covariates representing biotic and abiotic control of TOC. The measurement of SOC and their corresponding value of environmental covariate at same measurement locations were used to fit the mapping model. For the mapping we used a machine learning model called quantile regression forest. Mapping is made with Quantile regression forest, which is similar to the popular random forest algorithm for mapping. Instead of obtaining a single statistic, that is the mean prediction from the decision trees in the random forest, we report all the target values of the leaf node of the decision trees. With QRF, the prediction is thus not a single value but a cumulative distribution of the TOC prediction at each location, which can be used to compute empirical quantile estimates. All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 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
- Hierarchy level
- Dataset
- Title
- Total Soil Organic Carbon Content
- Website
-
https://aussoilsdsm.esoil.io/slga-version-2-products/total-soil-organic-carbon-content
Method documentation
Reference System Information
- Reference system identifier
- EPSG/EPSG:4326
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
- urn:uuid/405bdb14-e7d2-4570-93c0-35f75d635f14
- Language
- English
- Character encoding
- UTF8
Point of contact
Type of resource
- Resource scope
- Dataset
- Metadata linkage
-
https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/405bdb14-e7d2-4570-93c0-35f75d635f14
Point-of-truth metadata URL
- Date info (Creation)
- 2021-08-10T00:00:00
- Date info (Revision)
- 2022-12-19T00:20:42
Metadata standard
- Title
- ISO 19115-1:2014/AMD 1:2018 Geographic information - Metadata - Fundamentals
- Edition
- 1
Metadata standard
- Title
- ISO/TS 19115-3:2016
- Edition
- 1.0
Metadata standard
- Title
- ISO/TS 19157-2:2016
- Edition
- 1.0
- Title
- Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
- Date (published)
- 2021
- Edition
- 1.0
Identifier
Overviews
thumbnail
Spatial extent
N
S
E
W
Provided by
Associated resources
Not available