Modeling of Soil Organic Carbon Fractions Using Visible–Near‐Infrared Spectroscopy

Abstract

There is a pressing need for rapid and cost‐effective tools to estimate soil C across larger landscapes. Visible–near‐infrared diffuse reflectance spectroscopy (VNIRS) offers comparable levels of accuracy to conventional laboratory methods for estimating various soil properties. We used VNIRS to estimate soil total organic C (TC) and four organic C fractions in 141 samples collected in the Santa Fe River watershed of Florida. The C fractions measured were (in order of decreasing potential residence time in soils): recalcitrant C (RC), hydrolyzable C (HC), hot‐water‐soluble C (SC), and mineralizable C (MC). Soil samples were scanned in the visible–near‐infrared spectral range. Six preprocessing transformations were applied to the soil reflectance, and five multivariate techniques were tested to model soil TC and the organic C fractions: stepwise multiple linear regression (SMLR), principal components regression, partial least squares regression (PLSR), regression tree, and committee trees. Total organic C was estimated with the highest accuracy, obtaining a coefficient of determination using a validation set ( R v 2 ) of 0.86, followed by RC ( R v 2 = 0.82), both using PLSR. The SC fraction was modeled best by SMLR ( R v 2 = 0.70), while PLSR produced the best models of MC ( R v 2 = 0.65) and HC ( R v 2 = 0.40). The addition of TC as a predictor improved the VNIRS models of the soil organic C fractions. Our study indicates the suitability of VNIRS to quantify soil organic C pools with widely varying turnover times in soils, which are important in the context of C sequestration and climate change.