Interpretable Machine Learning and Remote Sensing Data Reveal Soil Biogeochemistry Patterns in Agricultural Systems

Soil condition represents a critical factor for ensuring sustainable agricultural development and food security. In this study, we examined the content of key soil properties and their patterns using an interpretable machine learning framework in combination with remote sensing data (Sentinel-2A) across several land use types in northwestern Russia. The analyzed soil properties in 64 samples included soil organic carbon (Corg), total nitrogen (N), mobile phosphorus (Pmob), total phosphorus (Ptot), and mobile potassium (Kmob) sampled across three land use types: cropland, hayfield, and forest. For machine learning interpretability, model-agnostic methods were utilized, including permutation and SHapley Additive exPlanations (SHAP) with spatial visualization. Our results revealed the highest concentrations of Corg (6.1 ± 4.3%), Kmob (78.3 ± 42.1%), and N (31.2 ± 14.5 mg/100 g) in forested areas, while both types of phosphorus (Ptot and Pmob) peaked in croplands (0.075 ± 0.024 and 0.023 ± 0.015%, respectively). The lowest values of Corg were observed in hayfields, and the lowest values of Kmob and N in croplands. Model validation demonstrated that Corg and N were predicted most accurately (R2 = 0.53 and 0.55, respectively), where SWIR bands from Sentinel-2A satellite imagery were key predictors. The generated soil property maps and spatial SHAP values clearly showed distinct patterns correlated with land use types due to distinct biogeochemical processes across landscapes. Our findings demonstrate how land management practices fundamentally alter soil parameters, creating diagnostic spectral signatures that can be captured through interpretable machine learning and remote sensing.