Predicting Copper and Lead Concentration in Crops Using Reflectance Spectroscopy Associated With Intrinsic Wavelength-Scale Decomposition Spectral Transformation

Hyperspectral remote sensing is a reliable solution for monitoring heavy metal pollution in crops. However, there are few studies on the spectral transformation and estimation of heavy metal content in crops using time-frequency analysis. In this study, intrinsic wavelength-scale decomposition (IWD) was proposed to decompose hyperspectral data to fully exploit the sensitive information implied in them and to investigate the feasibility of the detection of copper (Cu) and lead (Pb) in maize leaves. Leaf spectra and Cu
²⁺
and Pb
²⁺
contents were obtained from potted maize plants under Cu and Pb stress in the laboratory. After the spectral data were processed using IWD to obtain the proper rotation components (PRC
${i}$
), the characteristic bands were extracted, a Hankel matrix was constructed, and singular value decomposition (SVD) was performed. Finally, singular entropy information was obtained to characterize the heavy metal content. Singular entropy, with a higher correlation with Cu
²⁺
and Pb
²⁺
contents, was selected to establish the univariate and multivariate partial least squares regression (PLSR) models. The results showed the following:
(1)
the
$R^{2}$
of the univariate model for the prediction of copper and lead content was 0.68~0.81, and the
$RMSE$
was 0.99~7.03.
(2)
The
$R^{2}$
of the multivariate PLSR model was as high as 0.83, and the
$RMSE$
was as high as 0.83. This study showed that the characteristic bands can be effectively extracted by IWD spectral transformation, which provides a promising method for estimating heavy metal pollution in vegetation.