Using the characteristic parameters of Hilbert marginal spectrum for indirectly estimating copper content in maize leaves under copper stress

• Published in: Remote sensing letters 2019-11, Vol.10 (11), p.1067-1076
• Main Authors: Guo, Hui, Yang, Keming, Cheng, Long, Wang, Min
• Format: Article
• Language: English
• Subjects: Copper; Corn; Correlation analysis; Correlation coefficient; Correlation coefficients; Estimation; Leaves; Mathematical models; Parameter estimation; Parameters; Pollution; Prediction models; Reflectance
• ISSN: 2150-704X; 2150-7058
• DOI: 10.1080/2150704X.2019.1646932

The aim of this study is to test whether the Hilbert marginal spectrum characteristic parameters of maize leaves reflectance of 400-900 nm can effectively estimate copper (Cu) contents in maize leaves under copper stress. Firstly, the reflectance spectra of 11 stress levels were measured from maize leaves using a spectrometer under laboratory conditions. Secondly, we processed the reflectance and obtained the Hilbert marginal spectrum. We found that there were some differences among the Hilbert marginal spectrums. We then defined characteristic parameters of Marginal spectrum Surrounding Area (MSA), Marginal Spectrum Energy (MSE), Marginal Spectrum Mean (MSM) and Marginal Spectrum Amplitude Maximum (MSAM). In the end, we analyzed the correlations between the four characteristic parameters and copper contents in maize leaves by Pearson correlation coefficient (r). We established the prediction models for copper contents in maize leaves, and the models were also validated. The results suggested that the characteristic parameters could well characterize the weak information of copper pollution and spectral distortion in leaves reflectance. The four characteristic parameters had significant effectiveness in estimating copper contents in leaves, and the MSE is the best. The prediction model based on MSE has the highest accuracy with R 2 of 0.557 and RMSE of 3.619 μg g −1 .