Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

Abstract

Laboratory visible and near-infrared (Vis-NIR) spectroscopy with partial least squares (PLS) regression can be used to determine the soil carbon (C) content, and the waveband selection procedures can refine the predictive ability. However, individually selected wavebands are not always the same depending on the location, scale, and approach. To simplify the variable selection issue, some methods for selecting wavelength regions instead of individual wavebands have been proposed. In this study, we explore relevant wavelength regions for predicting the total carbon (TC) content of lowland and upland soils in Madagascar from Vis-NIR spectroscopy using a dynamic version of backward interval PLS (biPLS) regression. The predictive ability of dynamic biPLS was compared with that of standard full-spectrum PLS (FS-PLS) using the cross-validated coefficient of determination (R²), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). The biPLS models using reflectance (R² = 0.877, RMSE = 0.690) and first derivative reflectance (FDR) (R² = 0.940, RMSE = 0.494) data sets showed better predictive accuracy than the FS-PLS models using reflectance (R² = 0.826, RMSE = 0.809) and FDR (R² = 0.933, RMSE = 0.518) data sets, the spectral efficiency was improved. By using biPLS to predict soil TC, the model was simplified by using only four selected wavelength regions in the reflectance (400–490, 1402–1440, 1846–1980 and 2151–2283 nm) and FDR (652–687, 1322–1443, 1856–1985, and 2290–2400 nm) data sets, which yielded reliable (RPIQ > 2.5) predictions.