Title
Predicting Copper and Lead Concentration in Crops Using Reflectance Spectroscopy Associated With Intrinsic Wavelength-Scale Decomposition Spectral Transformation
Abstract
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 Cu2+ and Pb2+ 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 (PRCi), 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 Cu2+ and Pb2+ 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 similar to 0.81, and the RMSE was 0.99 similar to 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.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3174690
IEEE ACCESS
Keywords
DocType
Volume
Crop heavy metal pollution, hyperspectral analysis, intrinsic wavelength-scale decomposition (IWD)
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Jianhong Zhang100.34
Min Wang27627.77
Keming Yang301.35
Bing Wu421.46
Yaxing Li500.34
Yanru Li600.34
Qianqian Han700.34