Wooster, Martin J., Roberts, Gareth J., Giglio, Louis, Roy, David P., Freeborn, Patrick H., Boschetti, Luigi, Justice, Chris, Ichoku, Charles, Schroeder, Wilfrid, Davies, Diane, Smith, Alistair M.S., Setzer, Alberto, Csiszar, Ivan, Strydom, Tercia, Frost, Philip, Zhang, Tianran, Xu, Weidong, de Jong, Mark C., Johnston, Joshua M., Ellison, Luke, Vadrevu, Krishna, Sparks, Aaron M., Nguyen, Hannah, McCarty, Jessica, Tanpipat, Veerachai, Schmidt, Chris and San-Migeul-Ayanz, Jesus (2021) Satellite remote sensing of active fires: history and current status, applications and future requirements. Remote Sensing of Environment, [112694]. (doi:10.1016/j.rse.2021.112694).
Abstract
Landscape fires are a widespread and often unpredictable phenomenon having many beneficial effects in the Earth system but also sometimes posing a threat to ecosystems, human health and infrastructure. Routine and objective regional to global scale information on landscape fires can only be provided using satellite Earth observation (EO). Active fire remote sensing is based on the principle that as biomass burns it releases heat in the form of infrared radiation, which is able to be sensed remotely by suitably equipped spaceborne sensors imaging at the time the fire is burning. Since the early 1980’s, active fire remote sensing from space has mapped the location and time of fire occurrence, and more recently extended this to retrieve characteristics such as radiative power output. Due to the global extent and highly variable nature of landscape fires, satellite active fire products are very widely used in both science and operational applications, including in near real-time fire monitoring and estimation of rates of smoke emission. Here we review the history of active fire remote sensing, the physical basis and techniques used to detect and characterize active fires, and overview some of the key applications and satellite fire product delivery portals. Finally, we summarize the current status of this research area and highlight areas where further development in sensors, datasets and underpinning theory is required to address remaining gaps and to evolve this area of science and its application further.
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