Spatial Modelling of Organic Carbon in Burned Mountain Soils Using Hyperspectral Images, Field Datasets, and NIR Spectroscopy (Cantabrian Range; NW Spain)

Abstract

Soil organic matter is seriously affected by fires and suffers changes in stock, composition, and distribution. In the North‐West side of the Cantabrian Range (northern Spain) fires are very common. In order to develop a cartographic technique to map areas with high carbon stocks caused by fire, we test a technique based on calibrated VIS‐NIR soil organic carbon models and hyperspectral images. Total (TOC) and oxidizable carbon (OC) were measured in 89 soil samples. The samples were scanned with VIS‐NIR spectrometer (400–2,500 nm), and the spectra were resampled to the hyperspectral image channels. Spectroscopic models for TOC and OC were fitted (R2 > 0·81) using partial least squares regression (PLSR). The predictions were regionalized to the hyperspectral image and the results validated with a new soil population consisting of 12 Valeri plots collected in burned slopes of the study area under heather vegetation. In soil samples, TOC and OC values are highly correlated (R = 0·92), and the coefficients of the PLSR models have a similar pattern, which suggests similar organic components. Nevertheless, there are significant differences in the values of the regression coefficients, much higher in the TOC model except at 560 and 2,054 nm that might be interpreted as labile carbon components, and at 1,590 nm. At this wavelength the coefficient of TOC is positive and OC is negative, and it could be interpreted as hydrocarbons components present in the TOC model. Copyright © 2015 John Wiley & Sons, Ltd.

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

“…Several wavelengths betweeñ 2100-2500 nm are known to be sensitive to soil organic matter content [83,84], which can explain the relatively high correlations found in this region. The high correlation in the SWIR region is in accordance with the results obtained in a previous study in the same area by Fernández et al [38] using laboratory reflectance spectra. They found the SWIR, mainly between~2100-2350 nm, to be the most significant region for SOC prediction, with the most important wavelength at 2212 nm.…”

“…Only three out of 19 estimated SOC over areas partially covered by vegetation, all of them using PLSR [36][37][38]. The results obtained in this study using AHS data (R 2 ≥0.60 and RPD ≥ 1.60) were in the range of those found in the literature.…”

“…The red region of the spectrum was also relevant for the SOC and organic matter estimations in previous studies [8,51,82], and also the SWIR region, mainly between 2100-2500 nm [83,84]. The AHS channel at 679 nm can be associated with chlorophyll absorption [85], and channels at 2102 and 2150 nm with lignin and recalcitrant C [38,48,86].…”

“…Bartholomeus et al [37] used AHS data and the residual spectral unmixing technique to estimate SOC in fields partially covered by maize, and obtained slightly worse results in the validation (R 2 = 0.56 and RPD = 1.50). Fernández et al [38] calibrated PLSR models using laboratory reflectance spectra that were subsequently applied to an AHS image to estimate SOC. They obtained slightly better results (R 2 = 0.72-0.73 and RPD = 1.89-1.92) than those obtained in this study.…”

“…They obtained slightly better results (R 2 = 0.72-0.73 and RPD = 1.89-1.92) than those obtained in this study. However, the application of the method proposed in Fernández et al [38] is more complicated, as it requires the collection of laboratory reflectance spectra of the soil samples, as well as the determination of a correction factor to obtain soil maps from lab predictions. The model for organic matter prediction by Franceschini et al [36] showed comparable results (R 2 = 0.60 and RPD = 1.60) to those obtained here.…”