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Open AccessArticle

Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation

1
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia
4
Bushfire & Natural Hazards Cooperative Research Centre, Melbourne, VIC 3002, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1568; https://doi.org/10.3390/rs11131568
Received: 21 May 2019 / Revised: 27 June 2019 / Accepted: 29 June 2019 / Published: 2 July 2019
(This article belongs to the Special Issue Remote Sensing and Image Processing for Fire Science and Management)
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Abstract

Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation. View Full-Text
Keywords: fuel moisture content; dual polarimetric Sentinel-1A; bare soil backscatter linear model; vegetation backscatter water cloud model fuel moisture content; dual polarimetric Sentinel-1A; bare soil backscatter linear model; vegetation backscatter water cloud model
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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).
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Wang, L.; Quan, X.; He, B.; Yebra, M.; Xing, M.; Liu, X. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sens. 2019, 11, 1568.

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