为解决传统的土壤地球化学测量方法成本高、 效率低等问题, 研究了利用可见-近红外光谱技术检测土壤重金属含量的简易方法。 研究对西范坪矿区土壤反射光谱进行微分、 连续统去除等六种变换, 利用逐步回归法和皮尔逊相关系数选出对土壤铜含量敏感的特征波段, 组成综合特征变量集, 再应用不同的特征变量选取方法和参数建立估算模型并检验。 结果表明: 不同的光谱变换方法对土壤铜含量信息提取能力不同, 每种光谱变换都对应特定的敏感波谱区间; 基于综合光谱变换信息建立的土壤铜含量反演模型精度优于基于单种光谱变换信息建立的模型; 利用综合光谱变换信息建立土壤铜含量反演模型, 后向剔除法优于前向引入法和逐步回归法, 当Removal取0.20时得到最优回归模型, 其模型决定系数R2和预测模型决定系数R2pre分别达到了0.851和0.830, 建模均方根误差RMSEC和预测均方根误差RMSEP分别为0.349和0.468 mg·kg-1, 能较好地检测土壤铜含量, 同时为其他土壤重金属元素的光谱检测提供了思路。

In order to solve the problem of high cost and low efficiency by using the traditional soil geochemical survey methods, this paper studied the simple detection method of soil heavy metal content with visible and near-infrared reflectance spectroscopy. The study collected visible and near-infrared reflectance spectroscopy of soil samples in Xifanping mining area; then treated the reflectance spectroscopy with six mathematic changes such as differentials and continuum removal in advance; the next step was to select characteristic wavelengths that were sensitive to soil copper content by using stepwise regression method and Pearson correlation coefficient as set of comprehensive characteristic variables; finally, utilized different methods and parameters of characteristic variable selection to build the soil total copper content models and tested them. Results showed that: to extract the information of copper content in soil, the performance of different spectral transform methods varied, and each spectrum transform method corresponded to its certain sensitive spectral ranges; the inversion models based on the integrated spectrum transform information were better than that based on only one kind of spectrum transform information; as for establishing the prediction model of soil copper content by using the integrated spectrum transform information, backward elimination was better than forward selection and stepwise selection, and when the Removal is 0.20, the optimum model was obtained, its coefficients of determination（R2）and determination coefficients of prediction（R2pre）reached 0.851 and 0.830, root mean square error of calibration（RMSEC）and root mean square error of prediction（RMSEP）were 0.349 and 0.468 mg·kg-1. The model has a good precision, and it provides a train of thought for the detection of other soil heavy metal elements with visible and near-infrared reflectance spectroscopy.