Estimation of soil copper content based on fractional-order derivative spectroscopy and spectral characteristic band selection

Hyperspectral remote sensing is a rapid and nondestructive method to estimate the soil copper content. However, before establishing the spectral estimation model, it is crucial to preprocess the hyperspectral data to eliminate noise and highlight the spectral response characteristics of copper. The two commonly used spectral preprocessing approaches, i.e., the first- and second-order derivatives, may not provide sufficient information on the copper in the soil spectra. Therefore, this study investigates the potential of using the fractional-order derivative (FOD) of the spectra (FOD spectra) for estimating the soil copper content. A total of 170 soil samples were collected, and the soil reflectance spectra were measured outdoors using an ASD FieldSpec3 portable spectrometer. The soil copper content was obtained by chemical analysis in the laboratory. A quantitative estimation model of the soil copper content was established by combining the FOD spectra with different orders and using the partial least squares (PLS) method. The results revealed that the accuracy and prediction ability of the models using different orders of the FOD spectra varied significantly. The model using the 0.8-order FOD spectra performed the best, and the coefficient of determination (R-2) and the ratio of the performance to deviation (RPD) of the validation set were 0.6416 and 1.63, respectively. The performance of the model using three characteristic bands (2365.5 nm and 2375.5 nm of the 0.9-order derivatives and 864.5 nm of the 1.1-order derivatives) provided significantly better performance than utilizing all wavelength bands from 400 to 2400 nm. This model provided the optimum predictive ability (R-2: 0.6552 vs. 0.6416, RPD: 1.65 vs. 1.63) and was straightforward, requiring only three bands. These results show that it is feasible and practical to establish an accurate and rapid estimation model of the soil copper content using FOD spectra. (C) 2022 Elsevier B.V. All rights reserved.