农业机械学报2025，Vol.56Issue(8)：32-41,10.DOI:10.6041/j.issn.1000-1298.2025.08.003
不同植被覆盖条件下Sentinel-1/2数据融合监测土壤含盐量模型研究
Monitoring of Soil Salt Content during Different Growth Periods of Crops Based on Sentinel-1/2
摘要
Abstract
Accurately and rapidly acquiring soil salinity content(SSC)information is crucial for agricultural sustainable development.Satellite remote sensing technology has attracted extensive attention in SSC monitoring due to its advantage of large-scale synchronous monitoring,but its monitoring accuracy often faces challenges from multiple error sources such as vegetation coverage interference and irrigation events.SSC under different vegetation coverage conditions was monitored based on Sentinel-1/2 satellite data combined with ground-measured data,aiming to clarify the impact of different vegetation coverage on the accuracy of SSC remote sensing monitoring.Firstly,the full vegetation coverage period was divided into three stages(D1:early stage;D2:middle stage;D3:late stage)according to vegetation coverage,NDVI variation trends,and crop growth periods.Secondly,the sensitivity of variables(vegetation indices and polarization indices)to SSC at different soil depths was analyzed,and the variable importance in the projection(VIP)analysis algorithm was used for variable screening.Finally,machine learning algorithms(support vector machine(SVM),random forest(RF),and extreme learning machine(ELM)models)were integrated to generate SSC distribution maps for different soil depths in each stage.Results showed that variables had the highest correlation with SSC in D 2,followed by D3 and D1.Fusion of radar and optical remote sensing data contributed to SSC monitoring across different crop stages.The RF model proved optimal for SSC monitoring,with the highest accuracy(R2 of 0.79,RMSE of 1.62 g/kg)at 10～20 cm soil depth.Spatially,the southern part of the study area exhibited the most severe soil salinization.Vertically,SSC was the highest at 20～40 cm across all stages.Temporally,SSC in 0～10 cm and 10～20 cm layers was increased with crop growth,while SSC at 20～40 cm showed a decreasing trend.These findings provided a scientific basis for precise monitoring and prevention of regional soil salinization.关键词
Sentinel-1/2/土壤含盐量/作物生长时期/VIP分析/机器学习/植被覆盖度Key words
Sentinel-1/2/soil salt content/crop growth period/VIP/machine learning/fractional vegetation cover分类
农业科技引用本文复制引用
代天金,陈俊英,郭佳奇,白旭乾,钱龙,巴亚岚,张智韬..不同植被覆盖条件下Sentinel-1/2数据融合监测土壤含盐量模型研究[J].农业机械学报,2025,56(8):32-41,10.基金项目
国家自然科学基金项目(52279047) （52279047）
