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

Authors

Nathaniel W. Chaney, Princeton UniversityFollow
Eric F. Wood, Princeton UniversityFollow
Alexander B. McBratney, The University of Sydney
Jonathan W. Hempel, National Soil Survey Center
Travis W. Nauman, Southwest Biological Science Center
Colby W. Brungard, Utah State University
Nathan P. Odgers, The University of Sydney

Date of this Version

2016

Citation

Geoderma 274 (2016) 54–67

doi 10.1016/j.geoderma.2016.03.025

Comments

US government work

Abstract

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' 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.