典型铅锌矿区土壤重金属含量高光谱反演模型研究

Study on Hyperspectral Inversion Model of Soil Heavy Metals in Typical Lead-Zinc Mining Areas

矿区开采造成的土壤重金属污染严重影响作物产量、引发人体疾病;有效预防土壤重金属污染对健康的损害非常重要。高光谱快速、动态获取地物连续光谱信号的特点,为发展基于遥感的土壤重金属含量监测提供了新的思路。针对河北省涞源县典型铅锌矿区,实地采集矿区及周边土壤样本,基于SVC HR-1024i地物光谱仪(350~2500 nm)获取土壤光谱反射率,通过对光谱数据进行平滑、一阶导数、多元散射校正、标准正态变换、多元散射校正后一阶导数、标准正态变换后一阶导数六种光谱数据组合变换,使用差值指数、比值和归一化方法从六种预处理数据中提取光谱指数,通过实验室化学测试分析得到土壤重金属镉、铅、锌含量,对不同重金属元素使用不同光谱变换方式进行预处理,得到不同类型重金属元素的最优光谱变换方式。采用差值指数、比值指数和归一化植被指数,提取不同光谱指数下的最优波段组合,从而得到用于不同重金属元素建模使用的最优自变量。基于随机森林和偏最小二乘回归法分别构建重金属元素反演模型。研究表明,通过对光谱数据预处理,可以有效地降低噪声,增强光谱特征。从结果来看,经过预处理后光谱数据与重金属含量相关性有所提高。对不同重金属元素建模选择对其最优的光谱指数自变量,增加了反演建模的有效特征。对三种重金属镉、铅、锌利用随机森林算法和偏最小二乘回归法建立预测模型,最优模型的R 2分别达到了0.90、0.91、0.84,证实了该方法的有效性。该研究可为铅锌矿区土壤重金属含量反演建模提供依据,为矿区土壤重金属含量检测提供方法参考。

Soil heavy metal pollution caused by mining in mining areas seriously affects crop yield and causes human diseases.It is necessary to prevent soil heavy metal pollution from damaging health.Hyperspectral remote sensing can rapidly and dynamically acquire continuous spectra signals of ground objects,which provides a new idea for developing soil heavy metal content monitoring based on remote sensing.Aiming at the typical lead-zinc mining area in Laiyuan County,Hebei Province,soil samples from the mining area and surrounding areas are collected on-site,and the reflectance spectra of soil were obtained using SVC HR-1024i spectrometer(350~2500 nm).Through the spectral data smoothing,first derivative(FD),multivariate scattering correction(MSC),standard normal variate transform(SNV),first derivative after multivariate scattering correction(MSC+FD),and first derivative after standard normal variatetransform(SNV+FD),six kinds of spectral transformations were performed.The difference index(DI),ratioindex(RI),and normalizeddifference index(NDI)methods were used to extract the spectral indices from the six pretreated data.The contents of heavy metals cadmium(Cd),lead(Pb)and zinc(Zn)in soil were obtained through laboratory chemical testing and analysis.Different spectral transformation methods pretreated different heavy metals.The optimal spectral transformation methods for heavy metal elements were obtained.The difference index,ratio index,and normalized vegetation index were used to extract the optimal band combination under different spectral indices to get the optimal independent variables for modeling different heavy metals.The inversion models of heavy metal elements were constructed based on random forest and partial least square method.The research indicated that the noise could be effectively reduced,and the spectral characteristics were enhanced by pretreatment of spectral data.The results showed that the correlation between the spectral data and the heavy metal content was improved after the pretreatment.The optimal independent variables for different heavy metal elements were selected to increase the practical features of inversion modeling.Random forest algorithm and partial least squares regression method were used to establish prediction models for three heavy metals:cadmium(Cd),lead(Pb),and zinc(Zn).The R 2 of the optimal models reached 0.90,0.91,and 0.84,respectively,which confirmed the validity of this research method.This study can provide a basis for the inversion modeling of soil heavy metal content in lead-zinc mining areas and a method reference for detecting soil heavy metal content in mining areas.