Quantification of Soil Organic Carbon Using Mid- and Near-DRIFT Spectroscopy

New, rapid techniques to quantify different pools of soil organic matter (SOM) are needed to improve our understanding of the dynamics and spatio-temporal variability of SOM in terrestrial ecosystems. In this study, total organic carbon (TOC) and oxidizable organic-carbon (OC) fraction were predicted by mid- and near-IR spectroscopy in combination with partial least squares (PLS) regression. Oxidizable organic-carbon content was also quantified by a modified Walkley-Black method, and total organic carbon was measured by carbon analyzer. The TOC and OC was quantified using mid- and near-IR spectroscopy for a floodplain, forest soil and a Blackland Prairie soil. The floodplain soil is mainly composed of quartz and kaolinite, whereas Blackland Prairie soils contain high concentrations of smectitic clays and low to high concentrations of carbonate minerals. The total organic carbon of 68 soil samples from two Texas sites varied between 1.9 and 43.6 g kg-1 C, and the oxidizable organic carbon of 26 samples from floodplain soils was in the range of 0.5 to 13.3 g kg-1 C. TOC and OC of soil were successfully calibrated and predicted by the PLS regression method using mid- and near-IR spectroscopy. The correlation using mid-IR spectra for TOC (r = 0.96, RMSEV = 0.32 for calibration; r = 0.93, RMSEP = 0.44 for prediction) was about the same as the near-IR result (r = 0.95, RMSEV = 0.37; r = 0.93, RMSEP = 0.42). PLS1 regression model for quantification of OC with mid-IR region (r =0.97, RMSEV = 0.08; r = 0.92, RMSEP = 0.12) was slightly better than the result with near-IR region spectra (r = 0.95, RMSEV = 0.10; r = 0.90, RMSEP = 0.14). PLS model with spectral data showed better correlation than the univariate least square regression method (r = 0.88, RMSEC: 0.15; r = 0.83, RMSEP = 0.18) with TOC measured by the carbon analyzer. This study shows that the partial least squares method using mid- and near-IR spectra of neat soil samples can be used to predict both total organic carbon and oxidizable organic-carbon fraction as a fast and routine quantitative method.