1. College of Resources and Environment Sciences, Shijiazhuang University, Shijiazhuang 050035, China 2. Hebei Investigation Institute of Hydrogeology and Engineering Geology, Shijiazhuang 050021, China
为探讨土壤重金属含量的高光谱快速监测方法,以石家庄市水源保护区褐土为研究对象,基于土壤有机质敏感波段对应的多种光谱变换指标,采用偏最小二乘回归方法,建立了土壤重金属镉(Cd)的高光谱间接反演模型。结果表明,研究区土壤样本Cd含量平均值为0.220 mg/kg,处于严重污染水平; 有机质含量与Cd含量之间显著相关,两者存在一定的吸附赋存关系; 有机质原始光谱反射率对应的敏感波段为797 nm,各种光谱变换中倒数对数的一阶微分(absorbance transformation and first derivative,ATFD)与有机质含量的相关性最大,一阶微分(first derivative,FD)与有机质含量存在最大的正相关关系; 基于建模和验证样本分析,多光谱变换指标偏最小二乘回归模型优于单光谱变换指标偏最小二乘模型和多光谱变换指标逐步回归模型,模型解释变量为1 409 nm波段处的倒数对数的二阶微分(absorbance transformation and second derivative,ATSD)和1 396 nm波段处的FD,建模和验证样本R2分别达0.83和0.80。采用基于有机质光谱诊断特征建立多光谱变换指标集成估算模型来间接反演重金属Cd含量是可行的,所建最优模型可以为该地区重金属Cd的快速遥感监测提供参考。
In order to explore the feasibility of estimating the heavy metal cadmium (Cd) content in soil by hyperspectral data, the authors chose the cinnamon soil of Shijiazhuang water conservation area as the research object. Based on the multiple spectral transformation indexes corresponding to the sensitive bands of soil organic matter, the authors established the hyperspectral indirect inversion model of soil heavy metal Cd by partial least squares regression method. Some conclusions have been reached: the average Cd content of soil samples in the study area is 0.220 mg/kg, which is at the serious pollution level. There exists a significant correlation between organic matter content and Cd content, and there is a certain adsorption relationship. The sensitive band corresponding to the original spectral reflectance of organic matter is 797 nm. The correlation coefficient between the absorbance transform first derivative (ATFD) and the organic matter content is the largest among the various spectral transformations. The first derivative (FD) has the largest positive correlation with the organic matter. The modeling and verification sample analysis show that the multivariate partial least squares model is better than the univariate partial least squares model and multivariate linear stepwise regression model. The model explanatory variables are the absorbance transform second derivative (ATSD) of 1 409 nm and the FD of 1 396 nm, and the modeling and verification samples R 2 were 0.83 and 0.80. The research shows that it is feasible to estimate heavy metal Cd content indirectly by establishing multiple spectral transformation indexes estimation model based on spectral diagnostic features of organic matter. The optimal model can provide a reference for the rapid monitoring of heavy metal Cd in this area.
贺军亮, 韩超山, 韦锐, 周智勇, 东启亮. 基于偏最小二乘的土壤重金属镉间接反演模型[J]. 国土资源遥感, 2019, 31(4): 96-103.
Junliang HE, Chaoshan HAN, Rui WEI, Zhiyong ZHOU, Qiliang DONG. Research on indirect hyperspectral estimating model of heavy metal Cd based on partial least squares regression. Remote Sensing for Land & Resources, 2019, 31(4): 96-103.
Zhou J H, Huang R X, Fan H B , et al. A review on the progresses of remediation technologies for contaminated soils[J]. Research of Soil and Water Conservation, 2016,23(3):366-372.
Shi Z, Wang Q L, Peng J , et al. Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations[J]. Science China(Earth Sciences), 2014,44(5):978-988.
Liu S, Wu Q Y, Cao X J , et al. Pollution assessment and spatial distribution characteristics of heavy metals in soils of coal mining area in Longkou City[J]. Environmental Science, 2016,37(1):270-279.
Wang Y, Li X Q, Song Z H , et al. Review on the research advances of the pollution and bioremediation of heavy metals in the soil[J]. Journal of Safety and Environment, 2009,9(3):60-65.
Ma W C, Liu Y, Sun X C , et al. Transfer and transformation characteristics of cadmium from soil to Vetiveria zizanioides[J]. Acta Ecologica Sinica, 2016,36(11):3411-3418.
Kelimu A, Zhang D , et al. Calibration and validation of soil salinity estimation model based on measured hyperspectral and ASTER image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(12):144-150.
Liu H J, Zhang B, Liu D W , et al. Study on quantitatively remote sensing typical soils in Songnen Plain,Northeast China[J]. Journal of Remote Sensing, 2008,12(4):647-654.
[8]
Kemper T, Sommer S . Estimate of heavy metal contamination in soil after a mining accident using reflectance spectroscopy[J]. Environmental Science and Technology, 2002,36(12):2742-2747.
Guo Y K, Cao X Y, Shi Z G . Inversion model of total amount of soil heavy metal based on spectral characteristics of rice canopy[J]. Remote Sensing Information, 2015,30(3):116-123.
Cheng X F, Song T T, Chen Y , et al. Retrieval and analysis of heavy metal content in soil based on measured spectra in the Lanping Zn-Pb mining area,western Yunnan Province[J]. Acta Petrologica et Mineralogica, 2017,36(1):60-69.
[11]
吴昀昭 . 南京城郊农业土壤重金属污染的遥感地球化学基础研究[D]. 南京:南京大学, 2005.
Wu Y Z . Heavy Metal Pollution in Suburban Soils of the Nanjing Area:A Feasibility Study of Remote-Sensing Geochemistry[D]. Nanjing:Nanjing University, 2005.
[12]
Moros J, Vallejuelo F O D, Gredilla A ,et al.Use of reflectance infrared spectroscopy for monitoring the metal content of the estuarine sediments of the Nerbioi-Ibaizabal River (Metropolitan Bilbao,Bay of Biscay,Basque Country)[J]. Environmental Science and Technology, 2009,43(24):9314-9320.
He J L, Jiang J J, Sun Z W , et al. Studying on retrieval of soil heavy metal content using the organic matter identification index[J]. Journal of Agricultural Mechanization Research, 2009,31(9):22-25.
Lan Z Y, Liu Y . Research on indirect hyperspectral estimating model and the spatial distribution characteristics of heavy metal contents in basin soil of Lean River[J]. Geography and Geo-Information Science, 2015,31(3):26-31.
Wang F, Cao W T, Kang R F , et al. Study on monitoring of soil heavy metal Cr in golden mining areas based on field measured spectrum[J]. Environmental Pollution and Control, 2016,38(2):13-18.
Jiang Z L, Yang Y S, Sha J M . Application of GWR model in hyperspectral prediction of soil heavy metals[J]. Acta Geographica Sinica, 2017,72(3):533-544.
Qiao X X, Feng M C, Yang W D , et al. Effect of spectral transformation processes on the PLSR models of soil nitrogen[J]. Journal of Geo-Information Science, 2016,18(8):1123-1132.
Yu L, Hong Y S, Geng L , et al. Hyperspectral estimation of soil organic matter content based on partial least squares regression[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(14):103-109.
Liu W, Zhao Z, Yuan H F , et al. An optimal selection method of samples of calibration set and validation set for spectral multivariate analysis[J]. Spectroscopy and Spectral Analysis, 2014,34(4):947-951.
Hong Y S, Yu L, Geng L , et al. Using direct standardization algorithm to eliminate the effect of laboratory geometric parameters on soil hyperspectral data fluctuate characteristic[J]. Journal of Central China Normal University (Natural Sciences), 2016,50(2):303-308.
Song J Y, Fu K D, Su B , et al. Spatial distribution of heavy metal concentrations and pollution assessment in the bed loads of the Lancang River System[J]. Acta Geographica Sinica, 2013,68(3):389-397.
Yao N, Peng K G, Liu Z G , et al. Distribution and risk assessment of soil heavy metals in the north suburb of Shijiazhuang City[J]. Journal of Agro-Environment Science, 2014,33(2):313-321.
Zhang J J, Tian Y C, Zhu Y , et al. Spectral characteristics and estimation of organic matter contents of different soil types[J]. Scientia Agricultura Sinica, 2009,42(9):3154-3163.
2019, Vol. 31 
