Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices

Abstract Visible-near-infrared (Vis-NIR) spectroscopy makes it possible to estimate soil organic matter content (SOMC). Spectral pretreatment techniques have important significance in the quantitative analysis of SOMC. A total of 150 soil samples collected in northwestern China were used as data sets for calibration and validation. The SOMC values and reflectance spectra were measured in the laboratory. Fractional-order derivatives (FODs) (intervals of 0.05, range of 0–2) were used for soil spectral pretreatment, and a new three-band index (modified normalized difference index, MNDI) was constructed based on the band-optimization algorithm and the existing two-band exponential form (normalized difference index, NDI). Partial least square-support vector machine (PLS-SVM) models were calibrated using spectral parameters selected based on a single dimension (FOD), two-dimensional index (NDI) and three-dimensional index (MNDI) and subsequently applied to estimate SOMC. Three model evaluation parameters, namely, the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to interquartile range (RPIQ), were used to evaluate the estimation accuracy of the models. The results showed that with increased derivative order, the spectral strength gradually decreased, but the spectral detail increased. Furthermore, the correlation between FOD spectra and SOMC was significantly enhanced in the visible region, with the most obvious effect in the 1.05- to 1.45-order range. The PLS-SVM modeling results showed that the sensitivity and estimation accuracy of SOMC increased with increasing spectral synergy (i.e., 1D (FOD)