石油烃污染紫色土的可见-近红外光谱特征及其含量估算研究

高光谱遥感技术是一种有效的监测石油类污染手段，目前主要应用于海上溢油方面，而关于土壤石油烃污染的研究较少。针对土壤石油烃污染研究不足的现状，选取柴油、汽油和机油三种石油烃，开展了石油烃污染紫色土的光谱特征实验研究，分析了紫色土在不同种类石油烃污染及不同污染浓度条件下的光谱特征，提取了含有不同种类石油烃的紫色土光谱吸收特征波段。在此基础上，经过7种光谱变换和相关性分析，筛选出与石油烃含量最敏感的光谱变量，分别采用单变量回归法和多元逐步线性回归法建立了估算模型，并对模型进行了验证。研究表明：含有柴油、机油和汽油的光谱在1 200，1 700和2 300 nm附近均出现了吸收特征，光谱吸收深度表现为：机油>汽油>柴油；多元逐步线性回归法优于单变量回归法，其建立的柴油、机油和汽油的估算模型决定系数均大于0.95，校正均方根误差小于0.47，验证均方根误差小于0.56，估算精度较高。

Research on Visible-Near Inf rared Spectral Characterization of Purplish Soil Contaminated with Petroleum Hydrocarbon and Estimation of Pollutant Content

Hyperspectral remote sensing technology is an effective method to monitor petroleum contamina-tion .It is mainly used in offshore oil spill ,while few study has been focusing on soil petroleum-hydrocarbon contamination .In this case ,due to the shortage of research on soil petroleum-hydrocarbon contamination , three kinds of petroleum hydrocarbons are selected ,including diesel ,gasoline and motor oil ,to characterize the absorption features of petroleum hydrocarbons in spectra yielded from contaminated purplish soils in condi-tion of different types and different concentrations of petroleum hydrocarbons and extract the spectral absorp-tion characteristic band of soil contaminated by petroleum hydrocarbon .Based on this ,seven kinds of spectral transformations and correlation analysis were conducted to select the most sensitive spectral variables with pe-troleum hydrocarbon content .The estimation model was established via univariate regression and multiple stepwise linear regression (SMLR) respectively and verified ultimately .The results show that the spectral sig-natures of soil contaminated by diesel ,motor oil and gasoline are in the vicinity of 1200 ,1700 and 2300 nm , and the absorption depth is shown as follows :motor oil>gasoline>diesel .Multiple stepwise linear regression method is superior to univariate regression method .The coefficients of determination (R2 ) of diesel ,motor oil and gasoline are greater than 0.95 .Moreover ,the root mean square error of calibration (RMSEC) is less than 0.47 and the root mean square error of validation (RMSEC) is less than 0 .56 ,which demonstrates higher esti-mation accuracy .