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

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Abstract
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.
Funding Information
  • Russian Science Foundation (25-74-00002)

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