Spectrally Derived Soil Salinization Information Extraction and Analysis of Driving Factors: A Case Study of Zhanhua District, Yellow River Delta

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.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2026-05-17
DOI
https://doi.org/10.3390/rs18101612
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spectrally Derived Soil Salinization Information Extraction and Analysis of Driving Factors: A Case Study of Zhanhua District, Yellow River Delta

Na Zhang, Tianyi Wang, Sheng Ma, Jian Chen et al.
Remote Sensing
Soil Geostatistics and Mapping
article

Spectrally Derived Soil Salinization Information Extraction and Analysis of Driving Factors: A Case Study of Zhanhua District, Yellow River Delta

Na Zhang, Tianyi Wang, Sheng Ma, Jian Chen, Qiang Wu, Weixu Yang, Qiang Li
article en

Abstract

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.

Remote SensingVol. 18(10)
Northeast Normal University (CN), China University of Geosciences (Beijing) (CN), Shanghai Municipal Human Resources and Social Security Bureau (CN)
National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 8%
Soil Geostatistics and Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.