基于偏最小二乘的土壤重金属铜含量高光谱估算

为探究高光谱数据估算土壤重金属铜含量的可行性，以石家庄市水源保护区褐土为研究对象，对不同光谱变换数据与重金属铜含量做了相关分析，建立了土壤重金属铜的单光谱变换指标偏最小二乘模型和多光谱变换指标偏最小二乘模型。结果表明：光谱反射率（R）经倒数一阶微分（RTFD）变换后与铜含量的相关性有所提高；光谱敏感波段为418、427、435、446、490、673、1 909、1 920和2 221 nm，基本位于土壤氧化铁、粘土矿物的特征吸收区域；对土壤重金属铜含量估算效果最好的单光谱变换指标偏最小二乘模型为RTFD模型，其模型决定系数（R²）为0.649，均方根误差（RMSE）为1.477；多光谱变换指标偏最小二乘模型R²和RMSE分别为0.751和1.162，建模效果优于单光谱变换指标模型。研究结果可为北方地区褐土类型土壤重金属铜的高光谱估算提供借鉴。

Hyperspectral Estimation of Heavy Metal Cu Content in Soil based on Partial Least Square Method

In order to explore the feasibility of estimating the heavy metal Cu content in soil by hyperspectral data, based on the study of the cinnamon soil of the water source protected area in Shijiazhuang, the correlation analysis between the different spectral data and the heavy metal copper content was made. The univariate partial least squares model of soil heavy metal Cu and a partial least squares model of multivariate were established. The results showed that the correlation between the spectral reflectance and the Cu content was improved by the Reciprocal Transformation First Derivative (RTFD). The spectral sensitivity bands were 418, 427, 435, 446, 490, 673, 1 909, 1 920, 2 221 nm, which was located in the characteristic absorption region of soil iron oxide and clay minerals. The univariate partial least squares model with the best estimation effect on soil heavy metal Cu content was RTFD model, and its model determination coefficient R² was 0.649, Root Mean Square Error (RMSE) was 1.477. The multivariate partial least squares model R² and RMSE were 0.751 and 1.162, and the modeling effect was better than the univariate model. The research results can provide a reference for the hyperspectral estimation of heavy metal Cu in cinnamon soil in northern China.