基于GF-1数据的耕地土壤镉(Cd)含量遥感估算方法

Methodology for estimating Cd content in farmland soil based onGF-1 remote sensing images

本文采用多种光谱变换和回归分析方法探索了使用GF-1卫星影像监测耕地土壤镉(Cd)含量的可行性。首先针对获取的GF-1原始影像数据,在完成预处理及剔除植被信息后进行倒对数、平方根和反正弦平方根变换,生成4套光谱影像;然后分别用采样点5 m缓冲区内各套影像光谱统计值与Cd含量进行相关性分析和多种回归分析。选择模型决定系数最高(>95%)的反正弦平方根变换后的自适应重加权回归方法构建的线性回归模型作为遥感估算模型。遥感估算结果在稻田积水、边缘地带等出现了异常估算值;笔者分析原因后应用线性插值的方法得到最终估算结果。相关性分析和建模精度表明该方法是可行的,有望应用于实际土壤质量监测和土地管理中。

This study explores the feasibility of estimating soil cadmium(Cd)content in farmland using GF-1 remote sensing satellite imagery.Correlation and different regression analyses have been separately conducted with sample Cd content and logarithmic,square root,and inverse square root transformation of image spectral image,which is filtered out vegetation information in the pre-processed remote sensing images.The linear regression model,with an accuracy above 0.95,is selected using competitive adaptive reweighted resampling based on the inverse square root transformation.The remote sensing estimates results,however,revealed a significant number of anomalous values in areas such as ponds,flooded rice fields,rooftops,hardened road surfaces and so on.An interpolation is employed with neighboring normal estimates to replace these anomalies,resulting in the final estimated values.Correlation analysis and modeling accuracy assessments suggest that this method is feasible and holds promise for practical applications in soil quality monitoring and land management.