Soil and Landscape Grid Australia-Wide 3D Soil Property Maps (3" resolution) - Release 1 

The Soil Facility 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://isric.org/projects/globalsoilmapnet). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). Attributes included: Available Water Capacity; Bulk Density - Whole Earth; Clay; Effective Cation Exchange Capacity; pH - CaCl2; Silt; Sand; Total Nitrogen; Total Phosphorus. 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 3.0 (CC By); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. 

All Rights (including copyright) CSIRO 2014. 

Data not provided. 

The digital soil attribute maps and their uncertainties were generated by harmonising different sources of soil data collected from point locations and using a 3-dimensional spatial modelling technique. Soil inventory: The national soil site data originates from two sources: (i) A set collated with the assistance of all the Australian State and Territory soil agencies (Searle, 2014). The individual State soil databases were combined into a single database adhering to the NatSoil Site Schema (Jacquier et al., 2012). This database contains morphological and laboratory data for all the soil profiles publicly available within existing agency databases in 2013. (ii) Spectroscopic estimates of the soil attributes with the Australian visible–near infrared database (Viscarra Rossel and Webster, 2012) on soil samples collected for the National Geochemical Survey of Australia (NGSA) (de Caritat, P & Cooper, M, 2011). Harmonisation to standard depths: Data for each soil attribute, for all depths that were present in the inventory, was extracted and harmonised to the six standard depths using two different methods. When there were data from more than two depths, a mass preserving spline (Bishop et al., 1999) was fitted to derive the standard depths. When only two depths were present we used the imputation method described by Clifford et al. (2014). Spatial modelling: The digital soil maps were generated by a 3-dimensional data mining-kriging approach with Monte Carlo resampling to produce estimates of uncertainty. The approach uses statistical relationships between the observed soil attributes at point locations and continuous values of more than 40 environmental covariates (including remote sensing, climatic data, a digital elevation model and terrain derivatives, gamma radiometrics and other geophysical data), and kriging of their residuals. The Cubist data mining software (Rulequest Research., 2008) implemented in the software R (R Core Team, 2013) was used for the data mining and the gstat package (Pebesma, 2004) was used for the geostatistical modelling. These hybrid models produce quantitative estimates of soil properties. Uncertainties in both parts of the model were quantified and expressed as the 90% confidence limits. Descriptions of the approach are given in Viscarra Rossel et al. (2015a); Viscarra Rossel and Chen (2011) and Viscarra Rossel, (2011).