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Spatial Modelling of Organic Carbon in Burned Mountain Soils Using Hyperspectral Images, Field Datasets, and NIR Spectroscopy (Cantabrian Range; NW Spain)
Author(s) -
Fernández Susana,
Peón Juanjo,
Recondo Carmen,
Calleja Javier F.,
Guerrero César
Publication year - 2016
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.2452
Subject(s) - hyperspectral imaging , partial least squares regression , total organic carbon , soil carbon , environmental science , soil test , soil science , soil water , organic matter , soil organic matter , spatial distribution , carbon fibers , mineralogy , environmental chemistry , geology , chemistry , remote sensing , mathematics , statistics , organic chemistry , algorithm , composite number
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 ( R 2 > 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.