Unlocking Soil Salinity Prediction With Remote Sensing Indices and Environmental Insights

作者：Tehseen Javed, Zhang Jinzhu, Li Wenhao, Zhang Jihong, Liu Jian, Lin Haixia, Zhenhua Wang · 发表于：Land Degradation and Development · 年份：2025 · DOI：10.1002/ldr.5694 · 被引用次数：7 · 研究领域：Soil Geostatistics and Mapping、Soil and Land Suitability Analysis、Remote Sensing in Agriculture

ABSTRACT Despite 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 (EC e ) 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 R 2 values consistently below 0.04, indicating limited predictive power. PCA analysis revealed d...