POLARIS Soil Properties: 30‐m Probabilistic Maps of Soil Properties Over the Contiguous United States

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 R2 of 0.41, normalized root‐mean‐square error of 12%, and a normalized mean absolute error of 8.8%.

Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations

“…Dexter (2004), Koekkoek and Booltink (1999), and Rawls et al (1982) build global soil map. Chaney et al (2019) similarly employed RF to build a map of predicted soil properties over the United States. Recently Szabó et al (2019) have developed PTFs based on RF and BRT to map soil hydraulic properties across a watershed.…”

“…Providing uncertainty estimates in PTF predictions is important to assess the reliability of estimates (Schaap & Leij, 1998). Uncertainty estimates are also essential information in most applications such as use in land surface models (Baroni et al, 2017;Chaney et al, 2019;Folberth et al, 2016;Van Looy et al, 2017). Prediction intervals can be estimated by building an ensemble of models.…”

Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

“…Soil properties influence soil moisture dynamics across the spatial and temporal scale. The Probabilistic Remapping of Soil Survey Geographic (POLARIS) dataset was used [41]. The POLARIS provides a 30-m probabilistic estimation of soil properties at six different depths across the USA, which are spatially continuous and internally consistent.…”

Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine

“…Summary of Variables Used in RFML Including Time Period, Resolution, and Data Source All input variables were accessed through the GEE's data archive, except for the three 30-m soil property data sets from POLARIS (available at www.polaris. earth;Chaney et al, 2016Chaney et al, , 2019, which were manually uploaded to the GEE for RFML classification. 16 vegetation layers appear in the top 16 rows (EVI, GI, NDVI, and NDWI), 12 thermal-moisture layers follow the vegetation layers (SM, LST, STR1, and STR2), and 8 soil-climate variable layers are the remaining 8 rows in the table (preci, aridity, cropland, and three soil properties).…”