Title: Bottom-Up Digital Soil Mapping is More Accurate than Traditional Soil Mapping and Top-Down Digital Soil Mapping
Abstract:This study presents a regional digital soil mapping (DSM) product that used a bottom-up approach to create spatial soil predictions that were more accurate than one of the most accurate and detailed t...This study presents a regional digital soil mapping (DSM) product that used a bottom-up approach to create spatial soil predictions that were more accurate than one of the most accurate and detailed traditional soil mapping (TSM) products in the world, the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) map. Prior to this work, DSM products had yet to definitively outperform TSM products, except in locations where existing TSM maps were based on low investment (e.g., generalized national soil maps). While DSM has had established advantages, such as quantitative property prediction and cost efficiency, remaining challenges for widespread adoption include obtaining accuracy and practicality that exceed TSM-based maps in areas with histories of strong soil survey programs. In comparison with SSURGO, multiple top-down DSM products (SoilGrids 2.0, POLARIS, and Soil Properties and Class 100m Grids of the USA) were evaluated along with a locally developed, bottom-up DSM map called BUDSS (bottom-up detailed soil survey). These maps were compared for their predictions of clay, silt, sand, and organic matter (OM) content at set standard depth intervals to 200 cm. The TSM map outperformed 75% of the national and global top-down DSM maps. In contrast, 46% of the maps created through the bottom-up approach demonstrated appreciably higher accuracy than the TSM map. These results indicate that bottom-up DSM products are needed for superseding the accuracy of TSM products, which themselves are constructed from a bottom-up approach.Read More
Publication Year: 2023
Publication Date: 2023-01-01
Language: en
Type: preprint
Indexed In: ['crossref']
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