Spectrally Derived Soil Salinization Information Extraction and Analysis of Driving Factors: A Case Study of Zhanhua District, Yellow River Delta.
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| Title: | Spectrally Derived Soil Salinization Information Extraction and Analysis of Driving Factors: A Case Study of Zhanhua District, Yellow River Delta. |
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| Authors: | Wang, Tianyi1 (AUTHOR), Chen, Jian1,2 (AUTHOR) jianchen@cugb.edu.cn, Ma, Sheng1,3 (AUTHOR), Yang, Weixu1 (AUTHOR), Zhang, Na1,2 (AUTHOR), Li, Qiang2,3 (AUTHOR), Wu, Qiang3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1612. 25p. |
| Subjects: | Soil salinization, Remote sensing, Evaporative power, Land management, River deltas, Climate change models |
| Geographic Terms: | Yellow River Delta (China) |
| Abstract: | Highlights: What are the main findings? Among four remote sensing salinity index models (SDI1–SDI4), the SDI1 (SI-NDVI) model achieved the highest overall accuracy of 86.21% and was identified as the most suitable for soil salinization inversion in the Yellow River Delta region. Over the past 30 years, soil salinization in Zhanhua District exhibited a spatial pattern of "lighter in the south and heavier in the north," with a phased evolution from "severe in the north and mild in the south" to "overall expansion" and finally to "improvement in the north and optimization in the south." Evaporation was identified as the dominant driving factor (SHAP value = 0.3357), followed by precipitation and population density. What are the implications of the main findings? The optimal SDI1 model provides a reliable and cost-effective remote sensing tool for regional-scale soil salinization monitoring, supporting the formulation of zoning-based and differentiated engineering and ecological management strategies for saline–alkali land in coastal areas. The XGBoost-SHAP quantification of driving factors reveals the coupled effects of climate change and human activities on salinization. CMIP6 scenario projections indicate that the SSP1-2.6 low-emission pathway offers the greatest potential for mitigating soil salinization by 2100, providing scientific support for sustainable land management and climate adaptation policies. Understanding the spatiotemporal evolution and driving mechanisms of soil salinization in the Yellow River Delta is a key research focus in the comprehensive utilization of saline–alkali land. Taking Zhanhua District as the study area, this study extracted soil salinization information using four remote sensing salinity index models (SDI1, SDI2, SDI3, SDI4). Model accuracy was evaluated, and the optimal model (SDI1, with an overall accuracy of 86.21%) was selected to analyze the spatiotemporal dynamics of soil salinization from 1993 to 2023. The XGBoost-SHAP framework was then applied to identify and interpret the driving factors of salinization. Furthermore, future soil salinization trends under climate change were projected based on four scenarios from the Sixth Coupled Model Intercomparison Project (CMIP6), including SSP1-2.6 (low forcing), SSP2-4.5 (medium forcing), SSP3-7.0 (medium-to-high-forcing), and SSP5-8.5 (high forcing). The results show the following: (1) Spatially, soil salinization in Zhanhua District exhibits a pattern of being "lighter in the south and heavier in the north." Over the past 30 years, salinization has undergone a phased evolution characterized by a transition from "severe in the north and mild in the south" to "overall expansion" and finally to "improvement in the north and optimization in the south," while the proportional structure of salinization severity levels has remained relatively stable. (2) Among the driving factors, evaporation is the dominant contributor (SHAP value = 0.3357), followed by precipitation (0.1732) and population density (0.1518). Soil moisture, land use, and temperature exert moderate influences, while nighttime light intensity, slope, and elevation contribute relatively less. Overall, soil salinization is jointly controlled by climatic factors and human–nature interactions. (3) Among the future climate scenarios, the SSP1-2.6 low-emission scenario exhibits the most pronounced mitigation trend, with a further reduction in salinization intensity projected by 2100. This study provides a scientific basis and data support for formulating soil salinization control and saline–alkali land management strategies in Zhanhua District and the Yellow River Delta. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Among four remote sensing salinity index models (SDI1–SDI4), the SDI1 (SI-NDVI) model achieved the highest overall accuracy of 86.21% and was identified as the most suitable for soil salinization inversion in the Yellow River Delta region. Over the past 30 years, soil salinization in Zhanhua District exhibited a spatial pattern of "lighter in the south and heavier in the north," with a phased evolution from "severe in the north and mild in the south" to "overall expansion" and finally to "improvement in the north and optimization in the south." Evaporation was identified as the dominant driving factor (SHAP value = 0.3357), followed by precipitation and population density. What are the implications of the main findings? The optimal SDI1 model provides a reliable and cost-effective remote sensing tool for regional-scale soil salinization monitoring, supporting the formulation of zoning-based and differentiated engineering and ecological management strategies for saline–alkali land in coastal areas. The XGBoost-SHAP quantification of driving factors reveals the coupled effects of climate change and human activities on salinization. CMIP6 scenario projections indicate that the SSP1-2.6 low-emission pathway offers the greatest potential for mitigating soil salinization by 2100, providing scientific support for sustainable land management and climate adaptation policies. Understanding the spatiotemporal evolution and driving mechanisms of soil salinization in the Yellow River Delta is a key research focus in the comprehensive utilization of saline–alkali land. Taking Zhanhua District as the study area, this study extracted soil salinization information using four remote sensing salinity index models (SDI1, SDI2, SDI3, SDI4). Model accuracy was evaluated, and the optimal model (SDI1, with an overall accuracy of 86.21%) was selected to analyze the spatiotemporal dynamics of soil salinization from 1993 to 2023. The XGBoost-SHAP framework was then applied to identify and interpret the driving factors of salinization. Furthermore, future soil salinization trends under climate change were projected based on four scenarios from the Sixth Coupled Model Intercomparison Project (CMIP6), including SSP1-2.6 (low forcing), SSP2-4.5 (medium forcing), SSP3-7.0 (medium-to-high-forcing), and SSP5-8.5 (high forcing). The results show the following: (1) Spatially, soil salinization in Zhanhua District exhibits a pattern of being "lighter in the south and heavier in the north." Over the past 30 years, salinization has undergone a phased evolution characterized by a transition from "severe in the north and mild in the south" to "overall expansion" and finally to "improvement in the north and optimization in the south," while the proportional structure of salinization severity levels has remained relatively stable. (2) Among the driving factors, evaporation is the dominant contributor (SHAP value = 0.3357), followed by precipitation (0.1732) and population density (0.1518). Soil moisture, land use, and temperature exert moderate influences, while nighttime light intensity, slope, and elevation contribute relatively less. Overall, soil salinization is jointly controlled by climatic factors and human–nature interactions. (3) Among the future climate scenarios, the SSP1-2.6 low-emission scenario exhibits the most pronounced mitigation trend, with a further reduction in salinization intensity projected by 2100. This study provides a scientific basis and data support for formulating soil salinization control and saline–alkali land management strategies in Zhanhua District and the Yellow River Delta. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101612 |
