Unlocking Soil Salinity Prediction With Remote Sensing Indices and Environmental Insights

ABSTRACTDespite advances in salinity prediction, a knowledge gap exists in accurately integrating remote sensing indices and environmental factors for effective management strategies. Therefore, this study examines the relationship between soil salinity (ECe) and remote sensing (RS) indices, soil texture properties, and ecological features. Several statistical techniques, such as Pearson correlation, Geographically Weighted Regression (GWR), Principal Component Analysis (PCA), and SHapley Additive exPlanations (SHAP), were used to investigate the capability of these indices and indicators for the prediction of soil salinity. The study revealed that the Decision Tree (DT) showed the highest accuracy for soil salinity prediction among the machine learning models, while XGBoost exhibited lower predictive performance. Evaluating the environmental indices with ECe, the Normalized Difference Salinity Index (NDSI) showed the highest positive correlation with ECe (r = 0.88), reflecting its effectiveness in salinity prediction. Moderate positive correlations were observed with the Soil Salinity Index (SSI, r = 0.65), while the Bare Soil Index (BSI, r = −0.85) and Soil‐Adjusted Vegetation Index (SAVSI, r = −0.76) demonstrated strong negative correlations. Soil physicochemical properties, including clay, silt, sand, organic carbon, and bedrock, exhibited weak relationships with ECe, with R2 values consistently below 0.04, indicating limited predictive power. PCA analysis revealed distinct contributions of RS indices to ECe variability, with NDSI and SSI positively influencing salinity variability, whereas SAVSI contributed inversely, aligning negatively along PC1. SHAP analysis further reinforced the predictive dominance of RS indices, assigning the highest importance value to NDSI (0.61), followed by BSI (0.28) and SAVSI (0.08). In contrast, soil texture properties and organic carbon exhibited minimal significance, with importance values under 0.02. NDSI was further tested across low‐ and high‐salinity farms, consistently outperforming other indices. These findings highlight its advantage in improving salinity mapping management strategies and advancing precision agriculture/environmental planning through modern analytical approaches.