Abstract

This paper discusses the effectiveness of using a number of methods of preprocessing spectrometric data in the 325–1075-nm wavelength range in order to predict the concentration of organic soil carbon. Methods of preprocessing spectral data (moving-average filtering, Savitzky–Golay smoothing, calculating the first and second derivatives, and scaling) were consecutively applied to the spectral data of soils (with their natural texture and pulverized) to increase the reliability and effectiveness of the models. According to the criterion of maximizing the determination coefficient and minimizing the rms error as a cross check, the best method of predicting organic soil carbon turned out to be partial-least-squares regression with computation of the first derivatives of the original spectra (Rcv2=0.758RMSE_cv=0.492).

© 2019 Optical Society of America

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