Predicting Copper and Lead Concentration in Crops Using Reflectance Spectroscopy Associated With Intrinsic Wavelength-Scale Decomposition Spectral Transformation
نویسندگان
چکیده
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 content crops using time-frequency analysis. In this study, intrinsic wavelength-scale decomposition (IWD) was proposed to decompose hyperspectral data fully exploit sensitive information implied them investigate feasibility detection copper (Cu) lead (Pb) maize leaves. Leaf spectra Cu2+ Pb2+ contents were obtained from potted plants under Cu Pb stress laboratory. After processed IWD obtain proper rotation components (PRCi), characteristic bands extracted, Hankel matrix constructed, singular value (SVD) performed. Finally, entropy characterize content. Singular entropy, with higher correlation contents, selected establish univariate multivariate partial least squares regression (PLSR) models. The results showed following: (1) R2 model prediction 0.68~0.81, RMSE 0.99~7.03. (2)The PLSR as high 0.83, 0.83. This study that can be effectively extracted by transformation, which provides promising method estimating vegetation.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3174690