为探究高光谱遥感手段反演土壤Cu含量方法的可行性, 以湖南省某矿区为例, 利用ASD地物光谱仪和实验室电感耦合等离子发射光谱法测定83个土壤样品350～2 500 nm光谱信号和Cu含量。 在光谱重采样、 一阶/二阶微分、 标准正态变换预处理对比分析基础上, 分别采用主成分分析与相关分析对潜在建模变量进行初步筛选, 运用逐步回归方法确定最终模型变量, 建立土壤Cu含量反演模型, 基于最优模型识别Cu含量光谱指示特征波段。 结果表明, 相对于传统主成分分析方法, 标准正态变换后的光谱全要素主成分分析逐步回归建模方法因保留土壤样品弱光谱信号能有效提升土壤Cu含量估算能力, R2达086, 模型对于预测样本的估计效果较好, 建模样本和预测样本的残差分别为076和129, 且通过F检验; 360～400, 922～1 009, 1 833～1 890与2 200～2 500 nm波段对研究区土壤Cu含量有较好指示性。 研究结果将丰富南方矿区土壤Cu含量估算典型案例, 同时为发展基于高光谱遥感的土壤环境监测手段提供理论支撑。

To explore the feasibility of evaluating soil Cu content with Hyperspectral Remote Sensing method, 83 soil samples were collection from a certain diggings in Hunan Province. Using ASD field spectrometer and Induced Coupled Plasma Atomic Emission Spectrometry collecting the reflectance spectra and Cu content. The reflectance spectra were processing with several method: resampling, first/second derivative, standard normal variate. Based on the transformational spectra, potential modeling variables were selected by using principal component analysis and correlation analysis. Final model with stepwise regression were established. Important wavelengths were recognized that respond to Cu content based on the optimal model. The result showedthat, compared to traditional principal component analysis method, because of retaining the weak spectrum signal, principal component stepwise regression with standard normal variate spectra can improve the accuracy of Cu content estimation (R2=086), and the estimation of predicting samples is effective. The residual error of modeling samples and predicting samples is 076 and 129, and it passed the F test. In study area, the reflectance on 360~400, 922~1 009, 1 833~1 890 and 2 200~2 500 nm was indicative to Cu content. The study result will enrich a typical case of diggings in South of China, and provide theoretical support for developing method of soil environment monitor based on Hyperspectral Remote Sensing.