{"title":"基于光谱偏振特征和机器学习的干旱区干湿季节土壤盐渍化预测","authors":"Xiaobing Wang, Mireguli Ainiwaer, Aizemaitijiang Maimaitituersun, Jiaqi Zhang, Xayida Subi","doi":"10.1002/ldr.5635","DOIUrl":null,"url":null,"abstract":"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<sub>e</sub>). 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<sub>e</sub> 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 <i>R</i><sup>2</sup> = 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.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"18 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Soil Salinization in Arid Regions During Wet and Dry Seasons Based on Spectro-Polarimetric Features and Machine Learning\",\"authors\":\"Xiaobing Wang, Mireguli Ainiwaer, Aizemaitijiang Maimaitituersun, Jiaqi Zhang, Xayida Subi\",\"doi\":\"10.1002/ldr.5635\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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<sub>e</sub>). 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<sub>e</sub> 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 <i>R</i><sup>2</sup> = 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.\",\"PeriodicalId\":203,\"journal\":{\"name\":\"Land Degradation & Development\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Land Degradation & Development\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1002/ldr.5635\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Land Degradation & Development","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1002/ldr.5635","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Prediction of Soil Salinization in Arid Regions During Wet and Dry Seasons Based on Spectro-Polarimetric Features and Machine Learning
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 (ECe). 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 ECe 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 R2 = 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.
期刊介绍:
Land Degradation & Development is an international journal which seeks to promote rational study of the recognition, monitoring, control and rehabilitation of degradation in terrestrial environments. The journal focuses on:
- what land degradation is;
- what causes land degradation;
- the impacts of land degradation
- the scale of land degradation;
- the history, current status or future trends of land degradation;
- avoidance, mitigation and control of land degradation;
- remedial actions to rehabilitate or restore degraded land;
- sustainable land management.