一种基于分段偏最小二乘模型的土壤重金属遥感反演方法

土壤中重金属由于其毒性而成为最有害的环境污染物之一，利用遥感进行土壤重金属检测和分布制图是目前最为高效的手段。采用哨兵二号（Sentinel-2）多光谱影像与实测样品光谱数据，对山西省铜矿峪铜矿尾矿库及其周边农田土壤的铜（Cu）含量进行估算，利用68个土壤样品的反射光谱，优选出适合土壤铜含量预测的波段，结合分段偏最小二乘法（Piecewise Partial Least Squares Regression，P-PLSR），对土壤铜含量进行估算，将模型用于Sentinel-2影像获得了Cu含量的空间分布。通过P-PLSR对实测样品光谱建模反演Cu含量的决定系数（R²）为0.89，预测偏差比（RPD）为2.82；利用Sentinel-2多光谱影像获得了该区域Cu元素含量空间分布，其Cu含量的估算精度R²为0.74，RPD为1.73，Cu含量高值区空间分布与尾矿库关系密切。Sentinel-2多光谱数据具有高空间分辨率（10、20和60 m）、高时间分辨率和幅宽大（290 km）等优势，通过敏感波段选择并建立反演模型，可实现大范围土壤环境制图。

A Remote Sensing Estimation Method for Heavy Metals in Soil based on Piecewise Partial Least Squares Model

Heavy metals in soil are among the most harmful environmental pollutants due to their toxicity. Detecting and mapping the distribution of heavy metal using remote sensing technique is inexpensive and efficient. In this study， Sentinel-2 multispectral data and field spectroscopy were adopted to estimate soil copper （Cu） concentrations of the tailing reservoir of Tongkuangyu Copper deposit， Shanxi Province， China and the surrounding farmland soil. Sixty-eight soil samples were collected and their reflectance spectra were used to estimate Cu concentration in soil. Spectral index applicable to the prediction of Cu contents in soil was derived， united with piecewise partial least square regression （P-PLSR）， the soil Cu contents were estimated. The coefficient of determination （R²） and residual prediction deviation （RPD） for the model developed using lab-measured spectra were 0.89 and 2.81. The model was applied to the Sentinel-2 multispectral data and the spatial distribution map of Cu content was predicted with relatively high R² （0.83） and RPD （1.56）. The result could facilitate the development of remediation strategies in terms of environmental protection. Sentinel-2 multispectral data， due to its high spatial resolution （10 m， 20 m and 60 m）， and large swath width （290 km）， could provide an alternative method for large-scale soil environment monitoring through reasonable selection of sensitive bands.