Soil and Landscape Grid National Soil Attribute Maps - Bulk Density - Whole Earth - Release 2

This is Version 2 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.\n\nIt supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546EE212B0048\n\nThe map gives a modelled estimate of the spatial distribution of Bulk Density in soils across Australia.\n\nThe 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).\n\nDetailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html\n\nAttribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method;\nUnits: g/cm3;\nPeriod (temporal coverage; approximately): 1950-2021;\nSpatial resolution: 3 arc seconds (approx 90m);\nTotal number of gridded maps for this attribute: 18;\nNumber of pixels with coverage per layer: 2007M (49200 * 40800);\nData license : Creative Commons Attribution 4.0 (CC BY);\nTarget data standard: GlobalSoilMap specifications;\nFormat: Cloud Optimised GeoTIFF;\nLineage: 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. \n\nSince 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).\n\nThe basic steps of the work entailed.\n\nUse soil data federator to get pertinent soils observation data\n\nDevelop spatial pedotransfer function prediction whole soil bulk density using soil carbon and texture data.\n\nCompile measured and inferred whole soil bulk density data (86306 cases), then setting aside a dataset of 7500 cases for external model evaluation.\n\n 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.\n\nPredictive model uncertainties quantified using UNEEC approach (Uncertainty Estimation based on local errors and Clustering).\n\nQuantification of model extension limits derived using hybrid method involving multivariate convex hull analysis and count of observations.\n\nDigital soil maps with quantified uncertainties (5th and 95th prediction interval limits) and assessment of model extrapolation risk were produced at 90m resolution for the following depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm.\n\nAll 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/.\n\nCode - https://github.com/AusSoilsDSM/SLGA\nObservation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html\nCovariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html