Next Article in Journal
A New Method for Acquisition of High-Resolution Seabed Topography by Matching Seabed Classification Images
Previous Article in Journal
New Scheme for Validating Remote-Sensing Land Surface Temperature Products with Station Observations
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(12), 1211; doi:10.3390/rs9121211

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

1
Remote Sensing Applications Research Group (RSApps), 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
2
Institute of Natural Resources and Territorial Planning (INDUROT), University of Oviedo, Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain
3
Department of Geology, University of Oviedo, Jesús Arias de Velasco s/n, 33005 Oviedo, Asturias, Spain
4
GIS-Forest Group, Department of Organisms and Systems Biology, University of Oviedo, Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain
5
Remote Sensing Applications Research Group (RSApps), Department of Physics, Polytechnic School of Mieres, University of Oviedo, Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain
6
National Institute for Aerospace Technology (INTA), Carretera Torrejón a Ajalvir, km 4, 28850 Torrejón de Ardoz, Madrid, Spain
*
Author to whom correspondence should be addressed.
Received: 25 July 2017 / Revised: 20 November 2017 / Accepted: 21 November 2017 / Published: 24 November 2017
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
View Full-Text   |   Download PDF [6416 KB, uploaded 24 November 2017]   |  

Abstract

The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R2 = 0.60–0.62, while models for the Hyperion sensor showed R2 = 0.49–0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600–700 nm), and the short wave infrared region between ~2000–2250 nm. The use of SMLR and spectral indices based on reference channels at ~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method. View Full-Text
Keywords: topsoil organic carbon mapping; imaging spectroscopy; AHS; Hyperion; spectral indices topsoil organic carbon mapping; imaging spectroscopy; AHS; Hyperion; spectral indices
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Peón, J.; Recondo, C.; Fernández, S.; F. Calleja, J.; De Miguel, E.; Carretero, L. Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery. Remote Sens. 2017, 9, 1211.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top