Optimizing Bare Soil Mosaics for Clay Prediction via Environmental Covariates and Variable Selection

Spatial information on soil clay content addresses critical needs in precision agriculture and soil health assessment worldwide. This study utilizes a mapping workflow for topsoil clay (0–20 cm) across croplands in southern Russia using Sentinel-2 bare soil mosaics and regional covariates representing soil-forming factors. We generated 26 multi-temporal mosaics via per-pixel and per-date approaches and then tested five scenarios with regional covariates in combination with three variable selection techniques: variance inflation factor (VIF), recursive feature elimination (RFE), and modified greedy feature selection (MGFS). We found that among 26 temporal mosaics, the best single mosaic (scenario 1) explained 25% of the clay variation (RMSE = 10.03%, R2 = 0.25, RPD = 1.16). Using only regional covariates selected after VIF and RFE approaches (scenario 2) yielded comparable results (RMSE = 9.77%, R2 = 0.27, RPD = 1.18). Combination of the best bare soil mosaic and all regional covariates without the variable selection method (scenario 3) improved the predictions (RMSE = 9.45%, R2 = 0.33, RPD = 1.24), and with the VIF/RFE application (scenario 4), the model showed slightly worse accuracy (RMSE = 9.65%, R2 = 0.31, RPD = 1.21). MGFS implementation (scenario № 5) boosted the model performance and resulted in the best predictions (RMSE = 8.73%, R2 = 0.42, RPD = 1.34). NIR bands from the bare soil mosaic, and terrain attributes with Landsat and MODIS variables from the regional covariate set were the key variables. We demonstrate that combining multi-temporal Sentinel-2 mosaics with regional covariates, when paired with an appropriate selection strategy, yields superior clay predictions.