矿区复垦农田土壤重金属含量的高光谱反演分析(英文)

Estimation of Heavy Metal Concentrations in Reclaimed Mining Soils Using Reflectance Spectroscopy

以矿区复垦农田土壤为研究对象,利用实验室获取的土壤重金属元素砷(As)、锌(Zn)、铜(Cu)、铬(Cr)和铅(Pb)的含量与土壤可见近红外高光谱数据建立重金属元素含量的定量估算模型。为了保证模型预测的精度和稳定性,首先,对原始光谱数据进行平滑处理,并进行光谱变换,即:一阶导数,标准正态变量变换及连续统去除变换;然后,通过相关性分析提取不同变换光谱的特征波段;最后,将最小二乘支持向量机与传统的多元线性回归和偏最小二乘回归方法的结果相比较。研究表明:(1)以不同变换光谱数据建立反演模型均有较好的稳定性并达到一定精度,其中以最小二乘支持向量机方法优于偏最小二乘回归优于多元线性回归模型(除少数几个情况外);(2)从不同光谱变换数据中提取的光谱特征对反演模型结果有一定影响,其中以连续统去除和标准正态变量变换建模结果较好,一阶导数变换稍差。因此,利用高光谱遥感技术来定量估算土壤重金属含量是可行的,而且,必要的光谱预处理对提高估算模型的精度很有帮助。

A selection of soil samples from reclaimed mining areas were prepared to establish the quantitative inversion models of the soil heavy metal（As,Zn,Cu,Cr,and Pb）concentrations.The concentrations of the soil heavy metals and the visible and near-infrared spectra of the soil samples were obtained in a darkroom.Firstly,smoothing processing was used to smooth the noise in the original spectra,and the spectral transformation techniques of first derivative（FD）,continuum removal（CR）,and standard normal variate（SNV）were used to promote the model stability and the accuracy of the prediction.Through correlation analysis,the feature bands of the different transformed spectra were extracted.Finally,three different inversion models were adopted and compared,i.e.,traditional multiple linear regression（MLR）,partial least squares regression（PLSR）,and least squares support vector machines（LS-SVM）modeling.The results indicated that：（1）the stability and accuracy of the inversion models established by the different transformed spectra was high,in which LS-SVM was better than PLSR,and PLSR was better than MLR（except for a few cases）;and（2）the spectral features extracted from the different transformed spectra had a certain influence on the inversion model,in which the results based on CR transformation and SNV transformation were better than the FD transformation.Therefore,the quantitative estimation of heavy metal concentrations by the use of reflectance spectroscopy is feasible,and the pre-processing is essential to improve the accuracy of the model.