Validation of GOES-16 ABI and MSG SEVIRI active fire products

The recent deployment of the next generation of geostationary weather satellites provides an opportunity for the establishment of a robust global network of geostationary fire data that can greatly complement existing polar-orbiting satellite fire products. Among other benefits, geostationary satellites provide frequent sampling of diurnal variations in fire activity. Building on established satellite active fire data validation protocols, we used Landsat-8 Operational Land Imager (OLI) as reference fire data to validate the fire products derived from two geostationary satellite sensors: the Advanced Baseline Imager (ABI) on board the National Oceanic and Atmospheric Administration (NOAA) GOES-16 satellite (launched November 2016); and the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI), on board the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Second Generation (MSG) satellite series (multiple launch dates). The two primary algorithms associated with these geostationary active fire data sets are the Fire Detection and Characterization (FDC) product based on the heritage Wildfire Automated Biomass-Burning Algorithm (WF-ABBA) for the GOES-series, and the Fire Radiative Power (FRP-PIXEL) product based on the Fire Thermal Anomaly algorithm (FTA) for the MSG series. Our standardized validation method allowed for a direct inter-comparison between the complementary active fire datasets. Specifically, we present an error assessment of the detection probability (omission error) and false alarm rate (commission error) for two periods in 2017 and 2018 that include extensive fire activity in the respective full-disk sectors covered by each product. The results highlight (i) the restrictiveness of the FRP-PIXEL product (98% omission error) compared to the FDC product (84% omission error), and (ii) the elevated false alarm rate of FDC (88% commission error) compared to FRP-PIXEL (8% commission error). These validation results will be used to help support the development of a harmonized global multi-sensor active fire dataset to be integrated into the Global Wildfire Information System (GWIS).