@article {bnh-5688, title = {Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation}, journal = {Remote Sensing}, volume = {11}, year = {2019}, month = {07/2019}, 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.

}, keywords = {bare soil backscatter linear model, dual polarimetric Sentinel-1A, fuel moisture content, ignition, remote sensing, vegetation backscatter water cloud model}, doi = {https://doi.org/10.3390/rs11131568}, url = {https://www.mdpi.com/2072-4292/11/13/1568}, author = {Long Wang and Xingwen Quan and Binbin He and Marta Yebra and Minfeng Xing and Xiangzhuo Liu} } @article {bnh-6039, title = {Near real-time extracting wildfire spread rate from Himawari-8 satellite data}, journal = {Remote Sensing}, volume = {10}, year = {2018}, month = {10/2018}, pages = {1654}, abstract = {

Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real--time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5{\textendash}2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of {\textendash}0.75 m/s, mean absolute percent error of 33.20\% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.

}, keywords = {fire spread rate; fire center; fire behavior; Himawari-8; near real-time}, doi = {https://doi.org/10.3390/rs10101654}, url = {https://www.mdpi.com/2072-4292/10/10/1654}, author = {Xiangzhuo Liu and Binbin He and Xingwen Quan and Marta Yebra and Shi Qiu and Changming Yin and Zhanmang Liao and Hongguo Zhang} }