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

Abstract

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 2 =0.75; ratio of prediction to deviation [RPD]=2.03; root mean square error of crossvalidation [RMSECV] = 1.68 mg/cm 3 ), soil% C (r 2 =0.86; RPD=2.66; RMSECV=0.48%) and soil% N (r 2 =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.

Combustion and Spectral Methods for Quantifying Carbon and Nitrogen Concentrations in Pacific Northwest Douglas-Fir Forest Soils

“…In our study, soil C and N were successfully estimated using fewer than 100 calibration samples, an outcome similar to more recent studies (Vasques et al, 2008;Kusumo et al, 2011;Bartholomeus et al, 2008).…”

“…The high accuracy of SOC estimation results from the strong absorption of OM in the visible-range. Some soil N can be estimated due to functional groups existing in the spectra that are directly related to N (Kusumo, Hedley, Hedley, & Tuohy, 2011). In our analysis, the estimation of nitrogen appears to be largely indirect, and mostly due to the relationship of nitrogen with soil carbon.…”

VNIR Soil Spectroscopy for Field Soil Analysis

“…This reduces method error and advantageously assesses the soil property in the environment in which it exists (e.g., Cozzolino et al 2013;Weindorf et al 2012;Ebinger et al 2003;Pracilio et al 2006). Field visible-near infrared (VNIR) soil spectroscopy shows particular promise, with a significant increase in research activity in recent years (Soriano-Disla et al 2014;Stenberg et al 2010;Kusumo et al 2011). The method rapidly collects a unique reflectance spectrum from a soil surface: the reflectance at each nanometer dependent on vibrational frequencies of molecules containing carbon, hydrogen, nitrogen, and oxygen.…”

Sorting of crop residues and fossil bones from soil by NIR Hyperspectral Imaging

“…In the field of soil analyses, hyperspectral imaging has been applied to analyse soil organic matter and phosphorus [3][4][5][6][7], to determine maize and pasture root density [8][9], or to classify archaeological soils [10].…”