Predicting soil types and soil properties with limited data in the Uasin Gishu Plateau, Kenya

Abstract

Digital soil mapping (DSM) approaches can be used to create new soil maps or enhance existing maps, particularly in areas where only general soil maps are available. In this study, we utilized a knowledge-based inference soil mapping approach to develop a first-generation digital soil map for part of the Uasin Gishu Plateau in western Kenya. We calculated the following environmental covariates from the Shuttle Radar Topographic Mission (SRTM) 30 m digital elevation model (DEM): slope gradient, multi-resolution valley bottom flatness index (MrVBF), multi-resolution ridgetop flatness index (MrRTF), topographic position index (TPI), elevation, and profile curvature. These covariates were then used along with existing soil information and expert knowledge from soil scientists familiar with the area to produce new raster-based maps of soil types, effective soil depth, soil moisture storage capacity and soil drainage class. The soil type maps predicted using clustering analysis and fuzzy logic methods showed good agreement with field observations based on the overall accuracy values. The fuzzy logic map performed slightly better (kappa coefficient (k) = 0.68; overall accuracy = 0.76) than the map based on clustering analysis (k = 0.59; overall accuracy = 0.68). The accuracy for the effective soil depth fuzzy logic map was higher (R2 = 0.56; RMSE = 11; ME = 1.1) compared to the existing soil map (R2 = 0.34; RMSE = 27; ME = 8). Seven major soil types occur in the study area: Ferralsols, Nitisols, Gleysols, Luvisols, Acrisols, Cambisols and Regosols, according to the World Reference Base soil classification system. This study generated detailed and improved predictions of soil types and properties at 30 m grid resolution. These maps should be more useful for soil, crop and land use management decisions than existing maps.