Leveraging additional VIIRS information to improve wildfire tracking in the western US

Recent record-breaking fire activity in the western US poses clear threats to humans, ecosystems, and climate. Larger and faster fires increase the challenges for fire managers and further motivate the need for improved tracking of extreme fire behavior. There are also known limitations to our current ability to monitor fires from space. These include infrequent coverage from moderate resolution ($\le$ 1 km) sensors, smoke and cloud obscuration, omission of small or low-intensity fires, and atmospheric attenuation of fire radiative power (FRP). These effects diminish our ability to quantify fire behavior and emissions, including persistent burning behind the flaming fire front, particularly in ecosystems with high fuel loads. In this study, we examined the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery and data products to assess the utility of candidate fire pixels in addition to the low/nominal/high confidence 375-m fire detections already included in the active fire product. We found that these candidate pixels added 45% more daytime detections and 12% more nighttime detections for large fires in the western US 2020 fire season. Candidate fires were highly consistent with areas of flaming and smoldering fire activity identified by near-coincident airborne data as well as patterns of known active or candidate fires in sequential VIIRS overpasses, without significantly increasing false detections (commission errors). The candidate fire detections helped fill data gaps due to cloud obscuration during large fires that generated pyrocumulonimbus (pyroCb) clouds. Including this additional information also impacted estimates of fire activity, increasing fire persistence by 20% and FRP by 7% across our sample. Although the contribution from candidate fire detections to total FRP was relatively small, including these additional pixels could provide a more consistent estimate of fire emissions for smoke models and air quality forecasts by filling gaps in active fire information and improving the representation of smoldering fire activity. These results demonstrate the potential to augment the standard VIIRS product with candidate fire information for known large fire events to improve fire tracking and downstream products. Such approaches to leverage additional VIIRS information may be suitable for other biomass burning regions where global fire detection algorithms provide incomplete information for specific fire types and observing conditions.