Prediction Accuracy of Soil Organic Carbon from Ground Based Visible Near-Infrared Reflectance Spectroscopy

Abstract

The present study was conducted to predict soil organic carbon (SOC) from ground visible near-infrared (Vis–NIR, 400–2500 nm) spectroradiometer reflectance spectra. The objective was to study the effect of various pre-processing methods and prediction models on the accuracy of SOC estimated. Measured SOC content and reflectance spectra from pasture and cotton fields of Narrabri, Australia were used in the analysis. Reflectance spectra were pretreated with different smoothing methods such as: moving average, median filtering, gaussian smoothing and Savitzky Golay smoothing. A comparison between principal component regression, partial least square regression (PLSR) and artificial neural network models was carried out to get an optimum model for organic carbon prediction. The results indicate that PLSR model performs better with Savitzky Golay as the best pre-processing method for the study area, yielding $$ R_{\text{cal}}^{2} = \, 0.84 $$ , RPDcal = 2.55 and RPIQcal = 4.02 in the calibration set and $$ R_{\text{val}}^{2} = \, 0.77 $$ , RPDval = 2.17 and RPIQval = 3.19 in the validation set. The study recommends a suitable method in case of limited number of soil data. Based on the study, it can be said that properly pre-treated reflectance spectra show tremendous potential in soil organic carbon prediction.