Characterizing Live Fuel Moisture Content from Active and Passive Sensors in a Mediterranean Environment

• Published in: Forests Vol. 13; no. 11; p. 1846
• Main Authors: Tanase, Mihai A., Nova, Juan Pedro Gonzalez, Marino, Eva, Aponte, Cristina, Tomé, Jose Luis, Yáñez, Lucia, Madrigal, Javier, Guijarro, Mercedes, Hernando, Carmen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Artificial intelligence; Artificial satellites in remote sensing; Dielectric properties; Errors; Estimates; Flammability; Fuels; Influence; Joint use; live fuel moisture content; Machine learning; Moisture content; Moisture effects; Plant species; Radar; Radar data; Rain; Remote sensing; Risk assessment; Satellites; Sensors; Sentinel-1; Sentinel-2; Spatial discrimination; Spatial resolution; Synthetic aperture radar; Variables; Vegetation; Vegetation cover; Water content; Weather patterns; Wildfires; Spain
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f13111846

Live fuel moisture content (LFMC) influences many fire-related aspects, including flammability, ignition, and combustion. In addition, fire spread models are highly sensitive to LFMC values. Despite its importance, LFMC estimation is still elusive due to its dependence on plant species traits, local conditions, and weather patterns. Although LFMC mapping from active synthetic aperture radar has increased over the past years, their utility for LFMC estimation needs further analysis to include additional areas characterized by different vegetation species and fire regimes. This study extended the current knowledge using medium spatial resolution (20 m) time series acquired by active (Sentinel-1) and passive (Sentinel-2) sensors. Our results show that optical-based LFMC estimation may achieve acceptable accuracy (R2 = 0.55, MAE = 15.1%, RMSE = 19.7%) at moderate (20 m) spatial resolution. When ancillary information (e.g., vegetation cover) was added, LFMC estimation improved (R2 = 0.63, MAE = 13.4%). Contrary to other studies, incorporating Sentinel-1 radar data did not provide for improved LFMC estimates, while the use of SAR data alone resulted in increased estimation errors (R2 = 0.28, MAE = 19%, RMSE = 25%). For increased fire risk scenarios (LFMC < 120%), estimation errors improved (MAE = 9.1%, RMSE = 11.8%), suggesting that direct LFMC retrieval from satellite data may be achieved with high temporal and spatial detail.