Hyperspectral Estimation Model of Heavy Metal Arsenic in Soil
LI Zhi-yuan1,2, DENG Fan1*, HE Jun-liang2, WEI Wei1
1. School of Geosciences, Yangtze University, Wuhan 430100, China
2. College of Resources and Environment Sciences, Shijiazhuang University, Shijiazhuang 050035, China
摘要： 砷是严重危害人体的重金属之一。利用高光谱技术进行土壤重金属砷含量的估测具有很大的应用潜力，但受区域和土壤背景的影响，估算模型适用性和精度都会有很大的差异。针对石家庄市地表水源地保护区土壤砷含量的高光谱估算，在水源地保护区的主要采矿地和冶炼企业进行了土壤实地采样和实验室重金属分析，对土壤样本的原始光谱反射率采用Savitzky-Golay 7点平滑处理，进行一阶微分（FD）、二阶微分（SD）、倒数（RT）、倒数一阶微分（RTFD）、倒数二阶微分（RTSD）、倒数对数（AT）、倒数对数一阶微分（ATFD）、倒数对数二阶微分（ATSD）、连续统去除（CR）9种光谱变换后，再对重金属砷实测含量与经光谱变换后的光谱指标进行相关分析，并提取各光谱指标的最大敏感波段。运用多元线性逐步回归（MLSR）、单光谱变换指标偏最小二乘回归（U-PLSR）和多光谱变换指标偏最小二乘回归（M-PLSR）方法构建土壤重金属砷含量估算模型，最后通过相关系数r、均方根误差（root mean square error, RMSE）和统计值F来比对建模效果。结果表明：研究区部分土壤样本重金属砷含量已经出现了轻度污染，大部分样本处于污染的临界状态；经连续统去除变换后的光谱特征与砷的相关性最大，一阶微分与砷含量存在最大负相关性；相较于多元线性逐步回归和单光谱变换指标偏最小二乘回归，采取多光谱变换指标偏最小二乘回归方法土壤重金属砷含量模型估算值与实测值最为接近，建模R2达到0.852，RMSE和F值分别达到0.147和32.384，多光谱变换指标建模集成效果显著。因此研究结果可以为石家庄水源地保护区主要采矿地和冶炼企业重金属砷污染高光谱快速监测提供科学依据。
关键词：重金属砷；高光谱；多元线性逐步回归；偏最小二乘回归
Abstract：Arsenic is one of the heavy metals that seriously harm the human body. The estimation of arsenic content of heavy metals in soil by using hyperspectral technology has great potential for application. However, the applicability and accuracy of the estimation model will vary greatly due to the influence of the region and soil background. The hyperspectral estimation was done in the protection zone of surface water zone of Shijiazhuang. Soil field samples were conducted in the main mining sites and smelting enterprises in the study area and analyzed the heavy metal in the laboratory. After 7-point Savitzky-Golay smoothing of the original spectral reflectance of soil samples, 9 spectral transformations were performed: first derivative (FD), second derivative (SD), reciprocal transformation (RT), reciprocal first derivative (RTFD), reciprocal second derivative (RTSD), absorbance transformation (AT), absorbance transformation and first derivative (ATFD), absorbance transformation and second derivative (ATSD), and continuum removal (CR). Then, the measured content of heavy metal arsenic was correlated with the spectral indexes after spectral transformations, and the maximum sensitive band of each spectral index was extracted. In order to compare the estimation effect of each model, and find the optimal model, the multivariate linear stepwise regression (MLSR), univariate partial least squares regression (U-PLSR) and multivariate partial least squares regression (M-PLSR) method were used to construct the soil heavy metal arsenic content estimation model. The model of arsenic content estimation finally compared the modeling effect through correlation coefficient R2, root means square error (RMSE) and statistical value F. The results showed that: most of the samples in the study area were in the critical state of contamination, although some of the soil samples were already mildly contaminated with heavy metal arsenic. The maximum positive and negative correlations existed between CR and ATFD and arsenic content, respectively. Compared with MLSR and U-PLSR, the estimated value of M-PLSR was closest to the measured value, and the fitting samples’ R2, RMSE and F of M-PLSR were 0.852, 0.147 and 32.384, respectively, which indicated that the integration effect of multivariate transformation modeling was effective. Therefore, the research results can provide a scientific basis for rapid monitoring of heavy metal arsenic pollution in the region.
Key words：Heavy metal arsenic; Hyperspectrum; Multiple linear stepwise regression; Partial least squares regression
李志远，邓 帆，贺军亮，魏 薇. 土壤重金属砷的高光谱估算模型[J]. 光谱学与光谱分析, 2021, 41(09): 2872-2878.
LI Zhi-yuan, DENG Fan, HE Jun-liang, WEI Wei. Hyperspectral Estimation Model of Heavy Metal Arsenic in Soil. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2872-2878.
