Hyperspectral estimation of soil organic matter based on different spectral preprocessing techniques

In this study, the potentiality of visible and near-infrared reflectance spectroscopy to estimate soil organic matter was assessed. Six preprocessing methods were implemented to process the original spectra. The partial least-squares regression approach was also applied to construct predictive models and evaluate the optimal spectral preprocessing method. The significant wavelengths of soil organic matter were determined by using the correlation analysis and the partial least-squares regression analysis. The results were: (i) visible and near-infrared reflectance spectroscopy was proved to be an ideal approach in the soil organic matter estimation; (ii) different preprocessed spectra could improve their correlation with soil organic matter; the combination of first-order derivative and Savitzky–Golay smoothing method outperformed other preprocessing methods; (iii) the soil organic matter predictive models based on spectra processed by derivatives and Savitzky–Golay smoothing together presented a satisfactory accuracy, yielding the determination coefficient and root mean square error values of 0.986 and 0.077, respectively, for first-order derivative; and 0.973 and 0.105, respectively, for second-order derivative. The combination of first-order derivative and Savitzky–Golay smoothing was ultimately recommended the preferable preprocessing method; and (iv) the wavelengths of 417, 1853, 1000, and 2412?nm were determined as the significant wavelengths associated with soil organic matter. The study will provide a reference for the site specific management of agricultural inputs by using the visible and near-infrared reflectance spectroscopy technology.