煤矸石充填复垦重构土壤重金属含量高光谱反演

为研究煤矸石充填复垦土壤重金属含量快速有效的监测方法，以淮南创大生态园煤矸石充填复垦田间试验小区为研究区域，首先采用化学方法监测土壤(0～20 cm)重金属(Cu, Cr, As)含量，然后采用ASD(analytical spectral devices) FiSpec4型高光谱仪测量土壤样品的反射光谱，提取光谱特征，并对光谱进行一阶微分变换、二阶微分变换及倒数对数变换；将变换后的各光谱特征参数与监测的土壤重金属含量进行相关性分析，并依据相关性分析结果选择显著相关的波段作为相关因子供建模使用。采用多元逐步回归(stepwise multiple liner regression，SMLR)分析、偏最小二乘回归(partial least squares regression, PLSR)及人工神经网络(artificial neural network, ANN)三种方法分别建立基于光谱反射率估算土壤重金属含量的预测模型，并采用回归模型进行精度评定，然后确定各重金属含量的最佳预测模型。实验结果表明，经过微分变换的光谱波段与土壤重金属含量达到了显著相关；重金属Cu和Cr的一阶微分光谱的人工神经网络模型为最佳预测模型，重金属元素As的二阶微分光谱的偏最小二乘回归模型为最佳预测模型。

Hyperspectral Inversion of Heavy Metal Content in Coal Gangue Filling Reclamation Land

The research object of the paper is ,based on the Huainan Chuangda coal gangue filling reclamation experiment area , to analyze soil heavy metals (Cu ,As ,Cr) with the traditional sampling method .Reflectance spectra of soil samples measured by Analytical Spectral Devices FiSpec4 ,spectral features are extracted ,and the spectra are averaged with the first order differenti-al ,the second order differential transformation ,and the inverse logarithmic transformation ,etc .Correlation analysis of spectral characteristic parameters and heavy metal content in soil is conducted ,therefore ,the selection of the relevant bands is related to the relevant factors .Multivariate stepwise regression analysis ,partial least squares regression and artificial neural network are used to establish the prediction model of soil heavy metals by using soil spectral reflectance .The experimental results show that the spectral bands of the differential transformation are significantly correlated with the content of heavy metals .For heavy metal Cu and Cr ,the artificial neural network model of the first order differential spectrum is the best prediction model and the partial least squares regression model of the two order differential spectra of heavy metal elements is obtained by the best prediction results .