Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

摘要

This paper reports the development of a proximal sensing technique used to predict maize root density, soil carbon (C) and nitrogen (N) content from the visible and near-infrared (Vis-NIR) spectral reflectance of soil cores. Eighteen soil cores (0-60 cm depth with a 4.6 cm diameter) were collected from two sites within a field of 90-day-old maize silage; Kairanga silt loam and Kairanga fine sandy loam (Gley Soils). At each site, three replicate soil cores were taken at 0, 15 and 30 cm distance from the row of maize plants (rows were 60 cm apart). Each soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60 cm) and soil reflectance spectra were acquired from the freshly cut surface at each depth. A 1.5 cm soil slice was taken at each surface to obtain root mass and total soil C and N reference (measured) data. Root densities decreased with depth and distance from plant and were lower in the silt loam, which had the higher total C and N contents. Calibration models, developed using partial least squares regression (PLSR) between the first derivative of soil reflectance and the reference data, were able to predict with moderate accuracy the soil profile root density (r po = 0.75; ratio of prediction to deviation [RPD] = 2.03; root mean square error of cross-validation [RMSECV] = 1.68 mg/cmpd), soil% C (r po = 0.86; RPD = 2.66; RMSECV = 0.48%) and soil% N (r po = 0.81; RPD = 2.32; RMSECV = 0.05%) distribution patterns. The important wavelengths chosen by the PLSR model to predict root density were different to those chosen to predict soil C or N. In addition, predicted root densities were not strongly autocorrelated to soil C (r = 0.60) or N (r = 0.53) values, indicating that root density can be predicted independently from soil C. This research has identified a potential method for assessing root densities in field soils enabling study of their role in soil organic matter synthesis.