POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

A newcomplete map of soil series probabilities has been produced for the contiguous United States at a 30mspatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS\u27 accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security

Operationalising digital soil mapping – Lessons from Australia

Australia has advanced the science and application of Digital Soil Mapping (DSM). Over the past decade, DSM in Australia has evolved from being purely research focused to become ‘operational’, where it is embedded into many soil-agency land resource assessment programs around the country. This has resulted from a series of ‘drivers’, such as an increased need for better quality and more complete soil information, and ‘enablers’, such as existing soil information systems, covariate development, serendipitous project funding, collaborations, and Australian DSM ‘champions’. However, these accomplishments were not met without some barriers along the way, such as a need to demonstrate and prove the science to the soil science community, and rapidly enable the various soil agencies' capacity to implement DSM. The long history of soil mapping in Australia has influenced the evolution and culmination of the operational DSM procedures, products and infrastructure in widespread use today, which is highlighted by several recent and significant Australian operational DSM case-studies at various extents. A set of operational DSM ‘workflows’ and ‘lessons learnt’ have also emerged from Australian DSM applications, which may provide some useful information and templates for other countries hoping to fast-track their own operational DSM capacity. However, some persistent themes were identified, such as applicable scale, and communicating uncertainty and map quality to end-users, which will need further development to progress operational DSM