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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (4) : 96-103     DOI: 10.6046/gtzyyg.2019.04.13
Research on indirect hyperspectral estimating model of heavy metal Cd based on partial least squares regression
Junliang HE1, Chaoshan HAN1, Rui WEI1, Zhiyong ZHOU2, Qiliang DONG2
1. College of Resources and Environment Sciences, Shijiazhuang University, Shijiazhuang 050035, China
2. Hebei Investigation Institute of Hydrogeology and Engineering Geology, Shijiazhuang 050021, China
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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.

Keywords Cd      soil organic matter      hyperspectral      indirect estimating model      partial least squares regression     
:  TP79  
Issue Date: 03 December 2019
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Junliang HE
Chaoshan HAN
Zhiyong ZHOU
Qiliang DONG
Cite this article:   
Junliang HE,Chaoshan HAN,Rui WEI, et al. Research on indirect hyperspectral estimating model of heavy metal Cd based on partial least squares regression[J]. Remote Sensing for Land & Resources, 2019, 31(4): 96-103.
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Fig.1  Sampling point distribution in the study area
统计量 总体样本 建模样本 验证样本
样本数/个 69 46 23
最大值/(mg·kg-1) 0.359 0.343 0.359
最小值/(mg·kg-1) 0.138 0.138 0.156
平均值/(mg·kg-1) 0.220 0.215 0.229
标准差/(mg·kg-1) 0.048 0.043 0.055
变异系数/% 21.73 19.83 24.14
Tab.1  Statistical characteristics of heavy metal Cd in soil
P [1,2) [2,3) [3,4) ≥4
样本个数 5 45 15 4
Tab.2  Statistical analysis of pollution index of heavy metal Cd in soil
光谱变量 敏感波段/nm 相关系数
R 797 -0.468**②
RT 797 0.437**
AT 797 0.448**
FD 1 396 0.721**
SD 1 408 -0.601**
RTFD 837 -0.670**
RTSD 2 204 -0.394**
ATFD 1 394 -0.766**
ATSD 1 409 0.676**
CR 1 909 0.666**
Tab.3  Maximum correlation coefficients of soil organic matter content and spectral variables
Fig.2  Scatter plots of the two sets of samples of fitting and testing for MLSR
参数 建模样本 验证样本
R2 0.81 0.75
RMSE 0.02 0.03
RPD 2.31 1.23
Tab.4  Results of MLSR model
Fig.3  Scatter plots of the two sets of samples of fitting and testing for ATSD-U-PLSR
参数 建模样本 验证样本
R2 0.78 0.80
RMSE 0.003 0.006
RPD 14.40 9.10
Tab.5  Results of ATSD-U-PLSR model
Fig.4  Scatter plots of the two sets of samples of fitting and testing for FD-U-PLSR
参数 建模样本 验证样本
R2 0.24 0.41
RMSE 0.005 0.010
RPD 7.79 5.76
Tab.6  Results of FD-U-PLSR model
Fig.5  Scatter plots of the two sets of samples of fitting and testing for M-PLSR
参数 建模样本 验证样本
R2 0.83 0.80
RMSE 0.02 0.006
RPD 2.46 9.32
Tab.7  Results of M-PLSR model
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