Eighty-metre resolution 3D soil-attribute maps for Tasmania, Australia

Until recently, Tasmanian environmental modelling and assessments requiring important soil inputs relied on conventionally derived soil polygons that were mapped up to 75 years ago. In the `Wealth from Water' project, digital soil mapping (DSM) was used in a pilot project to map the suitability of 20 different agricultural enterprises over 70 000 ha. Following on from this, the Tasmanian Department of Primary Industries Parks Water and Environment has applied DSM to existing soil datasets to develop enterprise suitability predictions across the whole state in response to further expansion of irrigation schemes. The soil surfaces generated have conformed and contributed to the Terrestrial Ecosystem Research Network Soil and Landscape Grid of Australia, a superset of GlobalSoilMap.net specifications. The surfaces were generated at 80-m resolution for six standard depths and 13 soil properties (e.g. pH, EC, organic carbon, sand and silt percentages and coarse fragments), in addition to several Tasmanian enterprise-suitability soil-attribute parameters. The modelling used soil site data with available explanatory state-wide spatial variables, including the Shuttle Radar Topography Mission digital elevation model and derivatives, gamma-radiometrics, surface geology, and multi-spectral satellite imagery. The DSM has delivered realistic mapping for most attributes, with acceptable validation diagnostics and relatively low uncertainty ranges in data-rich areas, but performed marginally in terms of uncertainty ranges in areas such as the World Heritage-listed Southwest of the state, with a low existing soil site density. Version 1.0 soil-attribute maps form the foundations of a dynamic and evolving new infrastructure that will be improved and re-run with the future collection of new soil data. The Tasmanian mapping has provided a localised integration with the National Soil and Landscape Grid of Australia, and it will guide future investment in soil information capture by quantitatively targeting areas with both high uncertainties and important ecological or agricultural value.