Expert-based maps and highly detailed surface drainage models to support digital soil mapping

Soil maps are an important tool for agricultural planning and land management. Digital techniques have been used to create soil maps. However, most studies did not explore drainage network (DN) information on prediction models, which are related to soil variability. Thus, this study aims to evaluate the contribution of DN to predict soil classes using digital soil mapping techniques. We used a conventional soil class map (1:20,000) and environmental variables, such as drainage and relief attributes and satellite images, aiming to extrapolate the soil map to a larger area. The work was conducted in São Paulo State, Brazil. We created a point grid with 30 × 30 m resolution to extract the soil and variables information. We used these data to calibrate a random forest model along with cross-validation to optimize the model selection. The predicted soil classes for the 53,800-ha study area were determined on two levels according to the World Reference Base (WRB) soil classification system. The first level considered only soil groups (i.e. Acrisol and Ferralsol), while the second level considered the soil group and a qualifier (i.e. Chromic Acrisol and Rhodic Acrisol). We validated the maps using other conventional soils maps (internal validation) and field sampling points (external validation). After extrapolating the soil map, we validated the model s performance using field observations. In this case, the method reached an accuracy of 0.56 and kappa of 0.31 for the soil’s first level, and 0.38 and 0.25 for the second level. Regosols and Cambisols prediction was underestimated, lowering the accuracy and kappa results on the validation. However, Ferralsols reached accuracy and Acrisols reached around 70% accuracy. The drainage related attributes had the highest contribution to the model’s performance (accuracy = 56%) and improved the soil map extrapolation.