Depth-Specific Prediction of Coastal Soil Salinization Using Multi-Source Environmental Data and an Optimized GWO–RF–XGBoost Ensemble Model.
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| Title: | Depth-Specific Prediction of Coastal Soil Salinization Using Multi-Source Environmental Data and an Optimized GWO–RF–XGBoost Ensemble Model. |
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| Authors: | Wang, Yuanbo1 (AUTHOR), Yang, Xiao2 (AUTHOR) shawnyang@mail.sdu.edu.cn, Lv, Xingjun1 (AUTHOR), He, Wei1,2 (AUTHOR), Shao, Ming1 (AUTHOR), Liu, Hongmei1 (AUTHOR), Jia, Chao2 (AUTHOR) |
| Source: | Remote Sensing. Dec2025, Vol. 17 Issue 24, p4043. 28p. |
| Subjects: | Soil salinization, Salinity, Land management, Ensemble learning, Machine learning, Environmental indicators, Agricultural ecology, Soil dynamics |
| Geographic Terms: | Yellow River Delta (China) |
| Abstract: | Highlights: What are the main findings? A multi-source-driven GWO–RF–XGBoost model predicts soil salinity at multi-depth profiles. Distinct drivers control salinity at different soil layers, including NDVI, moisture, TDS, and pH. What are the implications of the main finding? The framework enables fine-scale salinity risk mapping and mechanistic understanding in coastal agroecosystems. This transferable, interpretable approach supports targeted mitigation and sustainable land management in salinity-prone regions worldwide. Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate the depth-specific dynamics of soil salinity in the Yellow River Delta, a vulnerable coastal agroecosystem. Using multi-source environmental predictors and 220 field samples harmonized to 30 m resolution, the hybrid Gray Wolf Optimizer–Random Forest–XGBoost model achieved high predictive accuracy for surface salinity (R2 = 0.91, RMSE = 0.03 g/kg, MAE = 0.02 g/kg). Spatial autocorrelation analysis (Global Moran's I = 0.25, p < 0.01) revealed pronounced clustering of high-salinity hotspots associated with seawater intrusion pathways and capillary rise. The results reveal distinct vertical control mechanisms: vegetation indices and soil water content dominate surface salinity, while total dissolved solids (TDS), pH, and groundwater depth increasingly influence middle and deep layers. By applying SHAP (SHapley Additive Explanations), we quantified nonlinear feature contributions and ranked key predictors across layers, offering mechanistic insights beyond conventional correlation. Our findings highlight the importance of depth-specific monitoring and intervention strategies and demonstrate how explainable machine learning can bridge the gap between black-box prediction and process understanding. This framework offers a generalizable framework that can be adapted to other coastal agroecosystems with similar hydro-environmental conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A multi-source-driven GWO–RF–XGBoost model predicts soil salinity at multi-depth profiles. Distinct drivers control salinity at different soil layers, including NDVI, moisture, TDS, and pH. What are the implications of the main finding? The framework enables fine-scale salinity risk mapping and mechanistic understanding in coastal agroecosystems. This transferable, interpretable approach supports targeted mitigation and sustainable land management in salinity-prone regions worldwide. Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate the depth-specific dynamics of soil salinity in the Yellow River Delta, a vulnerable coastal agroecosystem. Using multi-source environmental predictors and 220 field samples harmonized to 30 m resolution, the hybrid Gray Wolf Optimizer–Random Forest–XGBoost model achieved high predictive accuracy for surface salinity (R2 = 0.91, RMSE = 0.03 g/kg, MAE = 0.02 g/kg). Spatial autocorrelation analysis (Global Moran's I = 0.25, p < 0.01) revealed pronounced clustering of high-salinity hotspots associated with seawater intrusion pathways and capillary rise. The results reveal distinct vertical control mechanisms: vegetation indices and soil water content dominate surface salinity, while total dissolved solids (TDS), pH, and groundwater depth increasingly influence middle and deep layers. By applying SHAP (SHapley Additive Explanations), we quantified nonlinear feature contributions and ranked key predictors across layers, offering mechanistic insights beyond conventional correlation. Our findings highlight the importance of depth-specific monitoring and intervention strategies and demonstrate how explainable machine learning can bridge the gap between black-box prediction and process understanding. This framework offers a generalizable framework that can be adapted to other coastal agroecosystems with similar hydro-environmental conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17244043 |
