Prediction of Soil Properties at Field-Scale for Site-Specific Management: Model Comparison and Spatial Diagnostics

Abstract

This study compared machine learning (ML), geostatistical, and hybrid methods for predicting soil organic matter (SOM), cation exchange capacity (CEC), and nitrogen using remotely sensed, topographic, and soil survey covariates to support targeted soil management at the field-scale. Using nested cross-validation, we found broadly similar predictive performance across methods, with XGBoost (XGB) showing a slight advantage over kriging with external drift (KED) by reducing pooled standardized root mean square error (RMSE) from 0.603 to 0.593, corresponding to a 1.7% relative reduction. Incorporating XGB into a regression-kriging framework provided only marginal additional improvement, reducing pooled standardized RMSE to 0.583. These results suggest that hybridization does not necessarily provide substantial gains when the ML model and covariates already capture most predictable spatial variation. However, model diagnostics showed that geostatistical correction was most beneficial when residual spatial autocorrelation remained after the ML step. Sentinel-derived surface and vegetation indices were the most consistent predictors, while topography played a secondary role, and soil survey variables contributed minimally. Overall, combining ML and geostatistical tools offers a more robust framework than relying on either alone, because their relative advantages vary with site and soil property.