Development of Mid-Infrared Spectroscopic Feature-Based Indices to Quantify Soil Carbon Fractions

Abstract

Mid-infrared spectroscopy (MIRS) provides a rapid-throughput methodology in predicting many soil properties. The objective was to evaluate the performance of spectral feature-based indices to predict soil carbon fractions and compare it to partial least square regression (PLSR) model. We scanned 135 soil samples by MIRS and analyzed for total organic carbon (TOC), microbial biomass carbon (Cmic), hot-water extractable carbon (HWEC) and total inorganic carbon (TIC). Ninety samples were used for model calibration, while remaining 45 samples were further used for model validation. To develop spectral indices, peak area at specific absorbance feature related to labile organic carbon (PA 2930 cm–1) and carbonate (PA 2515 and 713 cm–1) was calculated and correlated to measured values of interest. PLSR resulted in successful calibrated models with R2 = 0.95, 0.99 for TOC, TIC, and moderate successful models with R2 = 0.85, 0.81 for Cmic and HWEC, respectively. The same results were also obtained for model validation. In terms of spectral indices, regression analyses between PA 2930 cm–1 and values of TOC, Cmic and HWEC was moderate successful with R2 values 0.85 to 0.88. Correlation between PA 2515 and 713 cm–1 and TIC resulted in a successful regression model with R2 = 0.98. As conclusion, precision level of spectral indices in predicting soil properties is almost equivalent to the PLSR models. However, correct large-scale application of the method requires excluding spectral mineral interference and model development by taking samples with a greater variability of soils and mineralogy.