Advancing the fire tracking paradigm to improve fire emissions estimates
Improvements in the sensitivity, spatial resolution, geolocation accuracy, and frequency of satellite active fire detections have ushered in a new era of individual fire tracking. Here, we explore how the fire tracking paradigm everages prior/contextual information and incorporates multiple satellites and sensors to drive improvements in fire detection, characterization, and quantitative measures of sub-daily fire behavior needed to advance estimates of fire emissions. For detection, the fire tracking approach provides prior information regarding multi-day fires to guide retrievals under clouds and dense smoke. These additional detections provide a more consistent assessment of fire activity over the lifetime of large fire events and especially during critical periods in fire growth, such as extreme fires that generate pyrocumulonimbus (pyroCB) clouds. Repeat observations from the VIIRS sensors on the Suomi-NPP, NOAA-20, and NOAA-21 satellites at 25-, 50-, and 100-minute intervals provide a diversity of view angles to further improve fire detection, estimate fire radiative power (FRP), and quantify rapid changes in fire behavior. The benefits from the current configuration of the VIIRS sensors underscores the potential for further improvements in fire detection and characterization from future additions to a virtual wildfire constellation built upon shared principles of free and open access to global fire data. Finally, the integration of improved VIIRS-based estimates of fire activity, FRP, and fire behavior with geostationary active fire observations and near real-time burned area data provides additional constraints to characterize diurnal variability in fire emissions, including enhanced nighttime emissions that may adversely impact air quality for surrounding communities. Combining information from multiple satellite platforms for individual fire events guides daily and sub-daily partitioning of fire emissions needed to update inventories and improve models.