Digital mapping of soil classes in Southeast Brazil: environmental covariate selection, accuracy, and uncertainty

Abstract

DSM has been used to meet the demand for soil information over large areas in more detailed resolution consuming less time. Soil-forming factors proxies (environmental covariates) are crucial to increase accuracy and provide new insights about the soil-landscape. DSM of the southern region of Minas Gerais state (52,982 km2) was generated. Soil profiles that comprise the legacy of decades of pedological surveys were gathered and associated with new (paleoclimatic, organic carbon, and airborne gamma-ray spectrometry) or less explored (digital terrain and climatic variables) environmental covariates under tropical conditions. An iterative routine of soil modeling, accuracy, uncertainty (entropy and MESS) assessment, and discussions concerning soil legacy was performed to equate pattern recognition with knowledge discovery from the random forest algorithm. The soil map was generated with 89% accuracy. The probability maps of the occurrence of the soil classes allowed soil-landscape insights. Less than 0.5% of the areas showed negative MESS, indicating an adequate representation of the soil samples concerning the environmental covariates. The most important environmental covariates were geophysical airborne gamma-ray spectrometry (K and eTh), distance from drainage channels, and paleoclimate (precipitation estimated from 22,000 years ago) consistent with the polygenetic soils.