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Prediction of Soil Salinization in Arid Regions During Wet and Dry Seasons Based on Spectro‐Polarimetric Features and Machine Learning

Bibliographic Data

ID21648735
AuthorsXiaobing Wang (0000-0002-5491-7418, College of Geographic Sciences and Tourism Xinjiang Normal University Urumqi China), Mireguli Ainiwaer (0000-0002-4044-9000, College of Geographic Sciences and Tourism Xinjiang Normal University Urumqi China, corresponding author), Aizemaitijiang Maimaitituersun (College of Hydrology and Water Resources Hohai University Nanjing China), Jiaqi Zhang (0000-0002-7999-8330, College of Geographic Sciences and Tourism Xinjiang Normal University Urumqi China), Xayida Subi (College of Geographic Sciences and Tourism Xinjiang Normal University Urumqi China)
Year2025
Volume36
Issue12
Pages4290-4303
Publication date2025-07-30
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.5635
OpenAlexW4410592591
LanguageEN
References cited52

Soil salinization is one of the main causes of soil degradation and ecosystem deterioration in arid regions, posing a serious threat to ecological environments and agricultural security. Understanding the factors influencing soil salinization is crucial for soil management and improvement. However, the sensitivity of soil salinization to seasonal changes has not been thoroughly studied in arid regions. Therefore, this study focuses on the Yanqi Basin, where 129 soil samples were collected (wet season of 51, dry season of 78) for laboratory analysis to determine the soil saturated extract conductivity (EC e ). Soil salinity feature variables were extracted from Sentinel‐1 radar remote sensing data, Sentinel‐2 optical remote sensing data, and digital elevation models (DEM). The Boruta algorithm was used to select feature variables, and the optimal feature variables were combined with Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models to construct prediction models. The results indicate: (1) Red‐edge spectral features (RE) can effectively predict soil salinization. In addition, the variables most correlated with EC e are elevation (DEM) and river network baseline (CNBL), mainly because the terrain in the study area is higher in the northwest and lower in the southeast, with flat farmland in the central region, where the movement of water and salt is significantly influenced by the terrain. (2) The RF model is the best prediction model in this study, with R 2 = 0.78, effectively revealing the spatial distribution of soil salinity during both the wet and dry seasons. (3) The degree of salinization in the wet season is significantly higher than in the dry season due to the combined effects of higher precipitation, lower vegetation cover, evaporation, and salt migration. (4) During both the dry and wet seasons, salinized soil is mainly concentrated along the shores of Bosten Lake, the Kaidu River, and Huangshui Ditch, while light salinization is distributed in the Gobi Desert areas. This study provides scientific evidence for the management and improvement of soil salinization caused by seasonal changes in arid regions

Arid · Geotechnical engineering · Physics · Polarimetry · Remote sensing · Soil salinity · Soil water · Environmental Science · Remote Sensing in Agriculture · Soil Geostatistics and Mapping · Soil Moisture and Remote Sensing · Geology · Soil Science

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