International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Evaluation of the spectral characteristics of five hyperspectral and multispectral sensors for soil organic carbon estimation in burned areas

Juanjo Peón A D , Susana Fernández B , Carmen Recondo A and Javier F. Calleja C
+ Author Affiliations
- Author Affiliations

A Area of Cartographic, Geodesic and Photogrammetric Engineering, Department of Mining Exploitation and Prospecting, University of Oviedo, Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain.

B Department of Geology, University of Oviedo, Jesús Arias de Velasco s/n, 33005 Oviedo, Asturias, Spain.

C Department of Physics, Polytechnic School of Mieres, University of Oviedo, Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain.

D Corresponding author. Email: juanjopeon@gmail.com

International Journal of Wildland Fire 26(3) 230-239 https://doi.org/10.1071/WF16122
Submitted: 3 July 2016  Accepted: 31 January 2017   Published: 28 February 2017

Abstract

Frequent wildfires in the north-west region of Spain affect soil organic matter. Soil properties can be estimated both spatially and temporally using remote sensing. A wide range of satellite and airborne hyperspectral and multispectral sensors are currently available. The spectral resolution varies substantially among sensors, making it difficult to identify the most suitable sensors and spectral regions for a specific application. This study aims to identify the sensors and wavelengths with the greatest potential for topsoil organic C mapping. Total (TOC) and oxidisable organic carbon (OC) content were measured in 89 soil samples collected in a mountain region of north-western Spain. Reflectance spectra of the samples in the spectral region 400–2450 nm were resampled to the bands of five sensors: Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS), Hyperion, Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS). Calibration models obtained using partial least-squares regression proved to be effective for hyperspectral sensors and also for the multispectral sensor MODIS (R2 = 0.75–0.89), which suggests that hyperspectral capability is not required to accurately predict topsoil organic C. Models based on Landsat performed well, but with an error ~30–45% greater than that obtained for the hyperspectral sensors and MODIS.

Additional keywords: AHS, CASI, Hyperion, Landsat, MODIS, PLSR, VIS-NIR-SWIR spectroscopy.


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