Soil-landscape mapping covering Western Australia at the best available scale (Version 05.02). It is a compilation of various surveys at different scales varying between 1:20,000 and 1:3,000,000. Mapping conforms to a nested hierarchy established to deal with the varying levels of information resulting from the variety of scales in mapping. For further information refer to Department of Agriculture Resource Management Technical Reports RMTR No. 280 and RMTR No. 313. Land capability and land quality attribution is included, refer to Department of Agriculture Resource Management Technical Report No. 298 for a description of the methodology employed.

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

Sixty one soils (soil types) represent the range of soils found across South Australia’s agricultural lands. Mapping shows the most common soil within each map unit, while more detailed proportion data are supplied for calculating respective areas of each soil type (spatial data statistics). Sixty one soils (soil types) represent the range of soils found across South Australia’s agricultural lands. Mapping shows the most common soil within each map unit, while more detailed proportion data are supplied for calculating respective areas of each soil type (spatial data statistics).

This map is one of a series of soil landscape maps that are intended for all of central and eastern NSW, based on standard 1:100,000 and 1:250,000 topographic sheets. The map provides an inventory of soil and landscape properties of the area and identifies major soil and landscape qualities and constraints. It integrates soil and topographic features into single units with relatively uniform land management requirements. Soils are described in terms of the Great Soil Group and the Northcote classification systems. Related Datasets: The dataset area is also covered by the mapping of the Soil and Land Resources of the Hawkesbury-Nepean Catchment, Soil and Land Resources of the Merriwa Plateau, Soil and Land Resources of the Hunter Region and Hydrogeological landscapes of NSW. Online Maps: This and related datasets can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area. Reference: Kovac M. and Lawrie J.M., 1991, Soil Landscapes of the Singleton 1:250,000 Sheet map and report, Soil Conservation Service of NSW, Sydney. Data and Resources

This dataset depicts a national map of available ASS mapping and ASS qualification inferred from surrogate datasets. ASS mapping is classified with a nationally consistent legend that includes risk assessment criteria and correlations between Australian and International Soil Classification Systems.

Existing digital datasets of ASS mapping have been sourced from each coastal state and territory and combined into a single national dataset. Original state classifications have been translated to a common national classification system by the respective creators of the original data and other experts. This component of the Atlas is referred to as the “Coastal” ASS mapping. The remainder of Australia beyond the extent of state ASS mapping has been “backfilled” with a provisional ASS classification inferred from national and state soils, hydrography and landscape coverages. This component is referred to as the “Inland” ASS mapping.

For the state Coastal ASS mapping, the mapping scale of source data ranges from 1:10K aerial photography in SA to 1:250K vegetation mapping in WA and NT, with most East coast mapping being at the 1:100K scale. For the backfilled inferred Inland ASS mapping the base scale is 1:2.5 million (except Tas.) overlaid with 1:250k hydography. As at 06/08, the Tasmanian inland mapping has been re-modelled using superior soil classification map derived from 1:100k landscape unit mapping.

NOTE: This is composite data layer sourced from best available data with polygons depicted at varying scales and classified with varying levels of confidence. Great care must be taken when interpreting this map and particular attention paid to the “map scale” and confidence rating of a given polygon. It is stressed that polygons rated with Confidence = 4 are provisional classifications inferred from surrogate data with no on ground verification. Also some fields contain a “-“, denoting that a qualification was not able to be made, usually because a necessary component of source mapping coverage did not extend to the given polygon. Lineage: Coastal ASS component:

Existing state CASS mapping was received and processed to varying degrees to conform to the NatCASS national ASS classification system. Spatially, all datasets were reprojected from their original projections to geographic GDA94. Classification of state mapping polygons to the NatCASS classification system was as follows. In the case of SA, NSW, Qld and WA it was a matter of directly translating the original state ASS classifications to the NatCASS classifications. These translations were undertaken by the creators of the state data and other experts within the respective states.

Due to the more broad classifications of the original Vic and Tas ASS mapping, polygons for these two states were initially translated to a NatCASS classification group (eg Tidal, Non-Tidal) by the data custodians then subsequently differentiated further through intersecting with other layers. These included the 3 second SRTM DEM and North Coast Mangrove mapping GIS datasets. The former being used to differentiate within the Non-Tidal zones (ie classes Ae-j and Be-j) and the latter used to differentiate the Tidal zones (ie Ab-d, Bb-d).

Mapping of the Tidal-Zone classes was augmented for all states except SA and NSW with 1:100K Coastal Waterways Geomorphic Habitat Mapping (Geoscience Australia). This dataset was used to infer additional areas of subaqueous material in subtidal wetland (class Aa & Ba) and Intertidal Flats (class Ab & Bb).

Inland ASS component:

Provisional Inland ASS classifications are derived from National and (in the case of Tasmania) state soil classification coverages combined with 1:250K series 3 Hydrography and Multiresolution Valley Bottom Floor Index (MrVBF).

Soil-landscape mapping covering Western Australia at the best available scale (Version February 2019) attributed with the proportional allocation of WA Soil Groups to each map unit. It is a …Show full descriptionSoil-landscape mapping covering Western Australia at the best available scale (Version February 2019) attributed with the proportional allocation of WA Soil Groups to each map unit. It is a compilation of various surveys at different scales varying between 1:20,000 and 1:3,000,000.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This is Version 1 of the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This is Version 1 of the Australian Soil Clay product of the Soil and Landscape Grid of 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: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (approximately 90 x 90 m pixels). These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia. Attribute Definition: 2 micrometre mass fraction of the less than 2 mm soil material determined using the pipette method; Units: %; Period (temporal coverage; approximately): 1950-2013; 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 x 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 3.0 (CC By); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Dataset History The National Digital Soil Property Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being: 1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2014a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2014a). Version 1 of the National Digital Soil Property Maps combines mapping from the: 1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps. These individual mapping products are also available in the CSIRO Data Access Portal (https://data.csiro.au). Please refer to these individual products for more detail on the DSM methods used. References: Specifications: Version 1 GlobalSoilMap.net products, Release 2.1, viewed 12/09/2014, http://www.globalsoilmap.net/specifications. Bishop, TFA, McBratney, AB & Laslett, GM 1999, 'Modelling soil attribute depth functions with equal-area quadratic smoothing splines', Geoderma, vol. 91, no. 1-2, pp. 27-45. http://dx.doi.org/10.1016/S0016-7061(99)00003-8. Breiman, L, Friedman, J, Stone, CJ & Olshen, RA 1984, Classification and Regression Trees, Wadsworth statistics/probability series, Wadsworth Belmont, Ca. Clifford, D, Dobbie, MJ & Searle, R 2014, 'Non-parametric imputation of properties for soil profiles with sparse observations', Geoderma, vol. 232-234, pp. 10-8. http://dx.doi.org/10.1016/j.geoderma.2014.04.026. Clifford, D, Searle, R & Holmes, KW 2015, 'Methods to merge disparate spatial estimates of soil attributes', Soil Research, in preparation. de Caritat, P & Cooper, M 2011, National Geochemical Survey of Australia: The Geochemical Atlas of Australia, Geoscience Australia, Record 2011/20 (2 Volumes), Canberra, 557 pp. http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_71973. DEWRN 2014, Mapping soil and land, Department of Environment, Water and Natural Resources, Government of South Australia, viewed 14/04/2014, http://www.environment.sa.gov.au/Knowledge_Bank/Information_data/soil-and-land/mapping-soil-and-land. Grunwald, S 2009, 'Multi-criteria characterization of recent digital soil mapping and modeling approaches', Geoderma, vol. 152, no. 3-4, pp. 195-207. http://dx.doi.org/10.1016/j.geoderma.2009.06.003. Hall, JAS, Maschmedt, DJ & Billing, NB 2009, The Soils of Southern South Australia, The South Australian Land and Soil Book Series, Volume 1; Geological Survey of South Australia, Bulletin 56, Volume 1, Department of Water, Land and Biodiversity Conservation, Government of South Australia. https://data.environment.sa.gov.au/Land/Land-Resources/Pages/Home.aspx. Holmes, KW, Griffin, TG & Odgers, NP 2015, 'Continental scale spatial disaggregation of legacy soil maps: evaluation over Western Australia', Soil Research, in preparation. Jacquier, D, Wilson, P, Griffin, T & Daniel, B 2012, Soil Information Transfer and Evaluation System (SITES) - Database design and exchange protocols, CSIRO Land and Water, Canberra. http://www.clw.csiro.au/aclep/publications/reports.htm. Kidd, D 2015, '80-metre Resolution 3D Soil Attribute Maps for Tasmania', Soil Research, in preparation. Kidd, DB, Malone, BP, McBratney, AB, Minasny, B & Webb, MA 2014, 'Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia', Soil Research, vol. 52, no. 2, pp. 107-19. http://dx.doi.org/10.1071/sr13100. Malone, BP, Minasny, B, Odgers, NP & McBratney, AB 2014, 'Using model averaging to combine soil property rasters from legacy soil maps and from point data', Geoderma, vol. 232, pp. 34-44. http://dx.doi.org/10.1016/j.geoderma.2014.04.033. McBratney, AB, Mendonça Santos, ML & Minasny, B 2003, 'On digital soil mapping', Geoderma, vol. 117, no. 1-2, pp. 3-52. http://dx.doi.org/10.1016/S0016-7061(03)00223-4. McKenzie, NJ, Jacquier, DW, Maschmedt, DJ, Griffin, EA & Brough, DM 2012, The Australian Soil Resource Information System (ASRIS) Technical Specifications, Revised Version 1.6, June 2012, The Australian Collaborative Land Evaluation Program. http://www.asris.csiro.au/downloads/ASRIS_Tech_Specs_201.6.pdf. McKenzie, NJ & Ryan, PJ 1999, 'Spatial prediction of soil properties using environmental correlation', Geoderma, vol. 89, no. 1-2, pp. 67-94. http://dx.doi.org/10.1016/s0016-7061(98)00137-2. Odgers, NP, Holmes, KW, Griffin, T & Liddicoat, C 2015a, 'Derivation of soil attribute estimations from legacy soil maps', Soil Research, in preparation. Odgers, NP, McBratney, AB & Minasny, B 2015, 'Digital soil property mapping and uncertainty estimation using soil class probability rasters', Geoderma, vol. 237-238, pp. 190-8. http://dx.doi.org/10.1016/j.geoderma.2014.09.009. Odgers, NP, Sun, W, McBratney, AB, Minasny, B & Clifford, D 2014, 'Disaggregating and harmonising soil map units through resampled classification trees', Geoderma, vol. 214-215, pp. 91-100. http://dx.doi.org/10.1016/j.geoderma.2013.09.024. Rodríguez, E, Morris, CS & Belz, JE 2006, 'A Global Assessment of the SRTM Performance', Photogrammetric Engineering & Remote Sensing, vol. 72, no. 3, pp. 249-60. Schoknecht, N & Pathan, S 2013, Soil groups of Western Australia: a simple guide to the main soils of Western Australia, 4th ed. Resource Management Technical Report 280, Department of Agriculture and Food Western Australia, Perth. http://archive.agric.wa.gov.au/PC_95446.html. Schoknecht, N, Tille, P & Purdie, B 2004, Soil-landscape mapping in south-western Australia: an overview of methodology and outputs, Resource Management Technical Report 280, Department of Agriculture, Government of Western Australia, Perth. Searle, R 2014, 'The Australian Site Data Collation to Support Global Soil Map', paper presented to GlobalSoilMap Conference 2013, Orleans, France, 7-9 October 2013, https://publications.csiro.au/rpr. Viscarra Rossel, RA 2011, 'Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra', Journal of Geophysical Research: Earth Surface, vol. 116, no. F4, p. F04023. http://dx.doi.org/10.1029/2011JF001977. Viscarra Rossel, RA & Chen, C 2011, 'Digitally mapping the information content of visible-near infrared spectra of surficial Australian soils', Remote Sensing of Environment, vol. 115, no. 6, pp. 1443-55. http://dx.doi.org/10.1016/j.rse.2011.02.004. Viscarra Rossel, RA, Chen, C, Grundy, M, Searle, R, Clifford, D & Campbell, PH 2015a, 'The Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project', Soil Research, in preparation. Viscarra Rossel, RA, Chen, H & Hicks, W 2015b, 'Prediction of spatial distribution of soil attributes to depth from Australian site and covariate data', Soil Research, in preparation. Viscarra Rossel, RA & Webster, R 2012, 'Predicting soil properties from the Australian soil visible-near infrared spectroscopic database', European Journal of Soil Science, vol. 63, no. 6, pp. 848-60. http://dx.doi.org/10.1111/j.1365-2389.2012.01495.x. Viscarra Rossel, RA, Webster, R, Bui, EN & Baldock, JA 2014, 'Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change', Global Change Biology, vol. 20, no. 9, pp. 2953-70. http://dx.doi.org/10.1111/gcb.12569. Dataset Citation CSIRO (2014) Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1. Bioregional Assessment Source Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/f8640540-4bb7-42ee-995a-219881e67705.

These products are derived from disaggregation of legacy soil mapping in the agricultural zone of South Australia using the DSMART tool (Odgers et al. 2014a); produced for the Soil and Landscape Grid of Australia Facility. There are 10 soil attribute products available from the Soil Facility: Available Water Capacity (AWC); Bulk Density - Whole Earth (BDw); Cation Exchange Capacity (CEC); Clay (CLY); Coarse Fragments (CFG); Electrical Conductivity (ECD); Organic Carbon (SOC); pH - CaCl2( pHc); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices (except for effective depth and total depth). Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the specifications of the GlobalSoilMap.

The DSMART tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes and corresponding soil properties. Legacy mapping was performed at 1:50,000 and 1:100,000 scales to delineate associated soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) kriging based on site data by Viscarra Rossel et al. (2014). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. 2014).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for South Australia’s agricultural zone was developed via SA’s State Land and Soil Mapping Program (DEWNR 2014, Hall et al. 2009). Sixty one soil classes (termed ‘subgroup soils’) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015b) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

South Australia’s national- or ASRIS-format soil mapping was used to provide reference soil properties. This dataset was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties of approximately 1500 regional variants of the original sixty one ‘subgroup soil’ classes. Equal area depth smoothing splines were applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (2015a).

This is Version 1 of the Australian Soil Total Phosphorus product of the Soil and Landscape Grid of 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: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: Total phosphorus; Units: %; Period (temporal coverage; approximately): 1950-2013; 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); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. For this soil attribute the Australia-wide three-dimensional Digital Soil Property Maps are the only maps available. Thus the modelling for this soil attribute only used Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a).

This is Version 1 of the Australian pH (Water) product of the Soil and Landscape Grid of Australia.

The map gives a modelled estimate of the spatial distribution of soil pH (1:5 soil water solution) 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: 0-5cm, 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: pH of a 1:5 soil water solution Units: None; Period (temporal coverage; approximately): 1950-2021; 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; 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-ph-15-water

We used a Random Forest model to fit the relationship between measurements and covariates. The Random Forest model uses the bootstrap resampling approach to iteratively develop the relationships between target variable and predictor variables.

Our modelling also included a repeated (n =50) bootstrap resampling approach but was different in that on each iteration the selected data which were also field data had to be converted to a ‘lab’ measurement. This ‘lab’ measurement was derived by drawing a value at random from the empirical distribution corresponding to the field measurement. In this way, we can incorporate into the modelling, the observed variability that is associated with field measurements, which also provides a seamless way to incorporate both data types.

The process of spatial modelling was relatively standard after the data integration step was done. Models were developed for each specified depth interval: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm. Our investigations also revealed there was some benefit to modelling the Random Forest model residuals using variograms. Together models were evaluated using a data set of size 10000 sites, meaning that the number of cases to evaluate models differed with each depth interval as more cases are found at the surface and near surface and drop off with increasing soil depth. We used the prediction interval coverage probability to assess the veracity of the uncertainty quantifications.

Soil pH mapping was output to the ~90m grid resolution in accordance with SLGA specifications.

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

Soil-landscape mapping covering Western Australia at the systems level of the soil-landscape mapping hierarchy. Systems derived from soil-landscape mapping (best available) Version April 2018. Intended scale of usage 1:250, 000. Mapping conforms to a nested hierarchy established to deal with the varying levels of information resulting from the variety of scales in mapping. For further information refer to Department of Agriculture Resource Management Technical Reports No. 280 and 313.

We used Digital Soil Mapping (DSM) technologies combined with the real-time collations of soil attribute data from TERN's recently developed Soil Data Federation System, to produce a map of Australian Soil Classification Soil Order classes with quantified estimates of mapping reliability at a 90m resolution.

The map gives an estimate of the spatial distribution of soil types across Australia.

Soil classes are based on The Australian Soil Classification - Second Edition by the National Committee on Soil and Terrain, R Isbell - https://ebooks.publish.csiro.au/content/australian-soil-classification-9781486304646

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Format: Cloud Optimised GeoTIFF; Lineage: The map was produced as per methods described at - https://aussoilsdsm.esoil.io/slga-version-2-products/australian-soil-classification-map

Soil classification data was extracted from the SoilDataFederator (SDF) - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html)

A total of 195,383 observations with either an Australian Soil Classification (ASC) or a Principal Profile Form (PPF) classification or a Great Soil Group (GSG) classification were extracted (Figure 1). Of these observations 130,570 of them had an ASC directly assigned by a pedologist. The remaining 64,813 observations either had a PPF or an ASC assigned to them by pedologists. The PPF and GSG classification where then transformed to an ASC using these remap tables.

The 90m raster covariate data was obtained from TERNs publicly available raster covariate stack - https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics .A parsimonious set of these covariates was used in the modelling.

We used the R "Ranger" Random Forest package to implement a machine learning model as per standard Digital Soil Mapping (DSM) methodologies.

The observed geographic locations in the ASC data set were used to extract cell values from the raster covariate stack using the R "raster" package. This data set was then divided into a 90/10% split of training and external validation sets. The training data was then bootstrapped sampled 50 times to create 50 bootstrap training sets. These training sets were then used to generate 50 Random Forest model realisations.

Using the CSIRO Pearcey High Performance Compute (HPC) cluster the Random Forest models were evaluated against the input covariate raster data stack. This was done for each 90m raster cell across the nation for each of the 50 bootstrapped model realisations. The modal ASC value across the 50 realisations for each cell was determined and assigned as the most probable soil type for that cell in the output raster. The ratio of the second most probable soil to the most probable soil was also calculated to generate a model confusion index, an estimate of the structural uncertainty in the Random Forest model.

The Australian Soil Resource Information System (ASRIS) contains a product that is a compilation of all existing polygon mapping conducted by state and federal soil survey agencies across all of Australia. This product is made up of a diverse range of field mapping products at a range of mapping scales. From this product we extracted all polygons that were mapped at a scale of 1:100,000 or finer, as defined in the Guidelines For Surveying Soil And Land Resources (Blue Book). Polygons mapped at this scale are high quality spatial estimates of the distribution of soil attributes. We then rasterised these polygon ASC values and merged these values into our final estimates of ASC, i.e., where an ASRIS 100,000 scale polygon exists it will replace the modelled ASC value.

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

Data gathered in the field during the sample collection phase of the National Geochemical Survey of Australia (NGSA) has been used to compile the Preliminary Soil pH map of Australia. The map, which was completed in late 2009, offers a first-order estimate of where acid or alkaline soil conditions are likely to be expected. It provides fundamental datasets that can be used for mineral exploration and resource potential evaluation, environmental monitoring, landuse policy development, and geomedical studies into the health of humans, animals and plants.

Digital soil maps (DSMs) are prepared through quantitative modelling techniques that are based on relationships between soil attributes and the environment. DSMs are presented over NSW for a range of key soil properties, including soil organic carbon (SOC), pH, cation exchange capacity, sum-of-bases, available phosphorous, bulk density, clay, silt and sand (total and fine). The maps are at 100 m spatial resolution and cover ten soil depth intervals down to 2 m, consistent with widely used Australian and international systems. Random Forest decision tree modelling techniques were applied. Validation results for the maps indicate generally moderate to high performance and effectiveness. Maps of mean plus upper 95% and lower 5% prediction limits are available. The maps provide at least a useful first approximation of these soil properties across the State. The products are described more fully in the technical report: Gray (2023), Digital soil mapping of key soil properties over NSW, version 2.0 (76p). The report and raster layers can be downloaded through the NSW environmental data portal SEED (https://www.seed.nsw.gov.au/) and are also viewable through the DPE soil and landscape spatial viewer eSPADE (http://espade.environment.nsw.gov.au ). All maps, including prediction limits and intervals, are also available through the DPE data broker. Data and Resources

Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1×1 km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders.

This is Version 1 of the Australian Soil Silt product of the Soil and Landscape Grid of 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: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: 2-200 μm mass fraction of the less than 2 mm soil material determined using the pipette method; Units: %; Period (temporal coverage; approximately): 1950-2013; 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); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF.

Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the National Digital Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The digital version of the Atlas of Australian Soils was created by NRIC (National Resource Information Centre) in 1991 from scanned tracings of the published hardcopy maps (1 - 10), Northcote et al. (1960 - 1968).

The Atlas of Australian Soils (Northcote et al, 1960-68) was compiled by CSIRO in the 1960's to provide a consistent national description of Australia's soils. It comprises a series of ten maps and associated explanatory notes, compiled by K.H. Northcote and others. The maps were published at a scale of 1:2,000,000, but the original compilation was at scales from 1:250,000 to 1:500,000.

Mapped units in the Atlas are soil landscapes, usually comprising a number of soil types. The explanatory notes include descriptions of soils landscapes and component soils. Soil classification for the Atlas is based on the Factual Key.

The Factual Key (Northcote 1979) was the most widely used soil classification scheme prior to the Australian Soil Classification (Isbell 2002). It dates from 1960 and was essentially based on a set of about 500 profiles largely from south-eastern Australia. It is an hierarchical scheme with 5 levels, the most detailed of which is the principal profile form (PPF). Most of the keying attributes are physical soil characteristics, and can be determined in the field.

The "mapunit" code contained within the digital dataset represents and links to the soil landscapes described in the explanatory notes. (explanatoryNotes.txt).The dominant and top 5 soils (as PPF classes) listed within the explanatory notes have been estimated from the text and are also included with this dataset (muppf5.txt).

Additional work by various groups has added some value to the dataset by providing look up tables that link to some interpretations of the mapping units or dominant soil type (PPF). Some examples of this include:

1. McKenzie, N. J. and Hook, J. (1992). Interpretations of the Atlas of Australian Soils. Consulting Report to the Environmental Resources Information Network (ERIN). CSIRO Division of Soils Technical Report 94/1992.

2. McKenzie NJ, Jacquier DW, Ashton LJ and Cresswell HP (2000) Estimation of soil properties using the Atlas of Australian Soils. CSIRO Land and Water Technical Report 11/00, February 2000.

3. Ashton, L.J. and McKenzie, N.J. (2001) Conversion of the Atlas of Australian Soils to the Australian Soil Classification, CSIRO Land and Water (unpublished).

Dataset History

The Digital version of the Atlas of Australian Soils was constructed from scanned tracings of the published hardcopy source maps, the thirteen sheets of the Atlas of Australian Soils. Use of the hard copies was necessary as the original printer's separates could not be located. The positional errors inherent in the original source maps would have been added and errors introduced by subsequent processes, beginning with the natural process of paper stretch. This was followed by the data processing steps which were, in order of execution: tracing, manual digitizing, transformation of coordinates and rubber sheeting to edge-match the digital versions of the adjacent sheets.

Dataset Citation

Bureau of Rural Sciences (2009) Digital Atlas of Australian Soils. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/9e7d2f5b-ff51-4f0f-898a-a55be8837828.

Soil acidity is a natural process that can be exacerbated in farming systems. Current knowledge and data on the extent and severity of acidic soils in south-western Victoria is limited. This makes inferences on the impacts to production across the region difficult. Furthermore, improved mapping is required in order to define the opportunities to address soil acidity in southern Victoria and increase production potential. The availability of soil site data managed in the Victorian Soil Information System (VSIS) and spatially exhaustive ancillary datasets (i.e. environmental covariate map data such as elevation, rainfall and gamma radiometrics) support the application of predictive modelling techniques to produce soil pH maps at finer scales and qualities previously unattainable.

The digital soil maps of soil pH for the South West region of Victoria have been produced by modelling the spatial relationships between points (soil sites) of measured or estimated soil pH and their environment (defined by a comprehensive set of covariates). A 10-fold cross validation procedure was used to produce average predictions for the upper, lower and mean values. The mapping provides predictions of soil pH at 50 m pixel resolution for six set depths from the surface down to two metres. The six set depths have been chosen to align to the Global Soil Map specifications, www.globalsoilmap.net.

In total, data from 3,668 sites were identified for application in spatial models across south-western Victoria. This data has been sourced from land studies dating back to the 1950s and the 670 samples collected by this project are now accessible as part of this larger dataset. Spatial covariate datasets using in modelling includes climate (e.g. annual rainfall, evaporation, Prescott index), landscape (e.g. clay mineral maps), organisms (e.g. MODIS time series, LANDSAT scenes), relief (e.g. elevation, slope, topographic wetness index) and parent material (e.g. terrain weathering index). In total, 71 covariate raster datasets have been used in generating soil pH maps.

The maps are for soil pH measured in a 1:5 soil-to-water suspension (pHw) with possible addition of a salt solution (typically Calcium chloride, CaCl2). The raster datasets (maps) include a mean, lower and upper uncertainty prediction for each depth interval.

Fifteen soil groups have been identified across southern South Australia, which are groupings of 61 soils (soil types). Mapping shows the most common soil group, while more detailed proportion data are supplied for calculating respective areas of each soil group (spatial data statistics).

This is Version 1 of the Australian Available Phosphorus product of the Soil and Landscape Grid of Australia.

The map gives a modelled estimate of the spatial distribution of available phosphorus 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: 0-5cm, 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: Available Phosphorus Units: mg/kg; 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; Lineage: This dataset models the spatial distribution of Available Phosphorus using a commonly measured analyte, bicarbonate - extractable phosphorus (Colwell P) (Method 9B1 & 9B2 - Rayment and Lyons 2010). It provides estimates of Colwell P across Australia for each Global Soil Map (GSM) depth interval at a 3 arcsecond resolution (80 - 100m pixel depending on where in Australia). The data is supplied as single band GeoTiff rasters and includes the 5th, 50th and 95th percentile predictions (Based on a 90% confidence interval) for each GSM depth.

Legacy Colwell P data currently stored in government agency soil databases in Australia that are from non-fertilised, non-cropped relatively undisturbed sites is being used to estimate AP. No new P data was collected for this project. Agency data was accessed using the newly developed Soil Data Federator Web API (Searle, pers.coms.). The Cowell P point data was combined with environmental covariates from the TERN national set to build a model of how Cowell P varies across Australia. Covariates were selected that best reflected the geography, geology, and climate of Australia. The model was built using the machine learning algorithm, Random Forests, which is commonly used in digital soil mapping in Australia.

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