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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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Abstract 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.
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Keywords
Cd
soil organic matter
hyperspectral
indirect estimating model
partial least squares regression
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Issue Date: 03 December 2019
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