The estimation of heavy metal content in leaf is important to the integration of remote sensing into evaluation the
ecological conditions in mining area. In this paper, correlation analysis and multivariable statistical methods were
used to build hyperspectral models for the heavy metal (e.g., Cu) estimation with independent variables such as
spectral reflectance, derivatives and ratio indices. Results showed that the heavy metals often display effects on
plants as they changed plant moisture content, the pigment content, the leaf structure, and so on. Stepwise Multiple
Regression Model predicted value and the actual value comparison showed that the model is stable, and the relative
deviation about single plant mostly below2%. The first and second order differential spectrums were employed on
three kinds of herbs synthesized also, the first order differential model proved better, and its relative deviation is
lower than 15%.
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