Evaluating Satellite Fire Detection Products and an Ensemble Approach for Estimating Burned Area in the United States

Abstract

Fire location and burning area are essential parameters for estimating fire emissions. However, ground-based fire data (such as fire perimeters from incident reports) are often not available with the timeliness required for real-time forecasting. Fire detection products derived from satellite instruments such as the GOES-16 Advanced Baseline Imager or MODIS, on the other hand, are available in near real-time. Using a ground fire dataset of 2699 fires during 2017–2019, we fit a series of linear models that use multiple satellite fire detection products (HMS aggregate fire product, GOES-16, MODIS, and VIIRS) to assess the ability of satellite data to detect and estimate total burned area. It was found that on average models fit with fire detections from GOES-16 products performed better than those developed from other satellites in the study (modelled R2 = 0.84 and predictive R2 = 0.88). However, no single satellite product was found to best estimate incident burned area, highlighting the need for an ensemble approach. With our proposed modelling ensemble, we demonstrate its ability to estimate burned area and suggest its further use in daily fire tracking and emissions-modeling frameworks.