Prediction of volumetric soil organic carbon from field-moist intact soil cores

This study investigated the potential for visible-near-infrared (vis-NIR) spectroscopy to predict locally volumetric soil organic carbon (SOC) from spectra recorded from field-moist soil cores. One hundred cores were collected from a 71-ha arable field. The vis-NIR spectra were collected every centimetre along the side of the cores to a depth of 0.3m. Cores were then divided into 0.1-m increments for laboratory analysis. Reference SOC measurements were used to calibrate three partial least-squares regression (PLSR) models for bulk density ((b)), gravimetric SOC (SOCg) and volumetric SOC (SOCv). Accurate predictions were obtained from averages of spectra from those 0.1-m increments for SOCg (ratio of performance to inter-quartile (RPIQ)=5.15; root mean square error (RMSE)=0.38%) and SOCv (RPIQ=5.25; RMSE=4.33kgm(-3)). The PLSR model for (b) performed least well, but still produced accurate results (RPIQ=3.76; RMSE=0.11Mgm(-3)). Predictions for (b) and SOCg were combined to compare indirect and direct predictions of SOCv. No statistical difference in accuracy between these approaches was detected, suggesting that the direct prediction of SOCv is possible. The PLSR models calibrated on the 10-cm depth intervals were also applied to the spectra originally recorded on a 1-cm depth increment. While a bigger bias was observed for 1-cm than for 10-cm predictions (1.13 and 0.19kgm(-3), respectively), the two populations of estimates were not distinguishable statistically. The study showed the potential for using vis-NIR spectroscopy on field-moist soil cores to predict SOC at high depth resolutions (1cm) with locally derived calibrations.