Water Resources Research · 2019 · 205 citations · 41 references
AgricultureEarth SciencesSoil SciencesSoil Organic MatterSoil ScienceSoil SurveySoil DensitySoil Hydraulic PropertiesSoil MappingSoil Physical PropertiesSoil Resource InventoryWater ContentEarth System ModelsSaturated Hydraulic ConductivitySoil Database DevelopmentSoil Property MappingSoil Property PredictionSoil Series InterpretationSoil Series MappingSoil Survey Database Compilation
Abstract Soils play a critical role in the cycling of water, energy, and carbon in the Earth system. Until recently, due primarily to a lack of soil property maps of a sufficiently high‐quality and spatial detail, a minor emphasis has been placed on providing high‐resolution measured soil parameter estimates for land surface models and hydrologic models. This study introduces Probabilistic Remapping of SSURGO (POLARIS) soil properties—a database of 30‐m probabilistic soil property maps over the contiguous United States (CONUS). The mapped variables over CONUS include soil texture, organic matter, pH, saturated hydraulic conductivity, Brooks‐Corey and Van Genuchten water retention curve parameters, bulk density, and saturated water content. POLARIS soil properties was assembled by (1) depth harmonizing and aggregating the pedons in the National Cooperative Soil Survey Soil Characterization Database and the components in Soil Survey Geographic Database into a database of 21,481 different soil series, each soil series having its own vertical profiles of different soil properties, (2) pruning the original POLARIS soil series maps using conventional soil maps to improve soil series prediction accuracy, and (3) merging the assembled soil series databases with the pruned POLARIS soil series maps to construct the soil property maps over CONUS. POLARIS soil properties includes 100‐bin histograms for each layer and variable per grid cell and a series of summary statistics at 30‐, 300‐, and 3,000‐m spatial resolution. Evaluation of POLARIS soil properties using in situ measurements shows an average R 2 of 0.41, normalized root‐mean‐square error of 12%, and a normalized mean absolute error of 8.8%.
41
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl, Jorge Mendes de Jesus, G.B.M. Heuvelink et al. · PLoS ONE · 2017 · 4.7K citations · Full text
Alex B. McBratney, Maria de Lourdes Mendonça-Santos, Budiman Minasny · Geoderma · 2003 · 3.6K citations
Guo‐Yue Niu, Zong‐Liang Yang, Kenneth E. Mitchell et al. · Journal of Geophysical Research Atmospheres · 2011 · 3.1K citations · Full text