Modeling the spatial distribution of sand, silt, and clay particles based on GlobalSoilMap and Limited Data

Many regions of Iran lack digital map of soil properties. The Chahardowli plain in western Iran is one of these areas. Due to the importance of sand, silt, and clay components, having quantitative and continuous data on abrupt changes in these two properties in this area is very critical. Therefore, to study sand, silt, and clay, samples were taken at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm, according to GlobalSoilMap. Finally, 145 samples were collected from 30 soil profiles. The significant covariates were selected by Random Forest Recursive Feature Elimination (RF-RFE). Relationships between these characteristics and environmental predictors were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. The accuracy and precision of the models used for all three particles showed that the RF model had the most accurate prediction with R2 and RMSE of 0.82 and 2.34 for clay, 0.80 and 3.87 for sand, and 0.85 and 2.89 for silt, respectively. In this study, terrain-based variables had a greater impact on improving accuracy than remote-sensing variables. The current study showed that even with limited information, digital mapping of sand, silt, and clay particles under GlobalSoilMap and the use of environmental factors can provide acceptable results.