Development of a multi-temporal Kalman filter approach to geostationary active fire detection & fire radiative power (FRP) estimation

Abstract Most active fire detection algorithms applied to data from geostationary Earth Observation (EO) satellites are adjustments of those originally developed for polar-orbiting systems, and thus the high temporal imaging frequencies offered from geostationary systems are often not fully utilized within such detection approaches. Here we present a new active fire detection algorithm that fully exploits geostationary data's temporal dimension, including both for detecting actively burning fires and quantifying their fire radiative power (FRP). The approach uses a robust matching algorithm to model each pixels diurnal temperature cycle (DTC) in the middle infra-red (MIR) spectral band, the most important band for active fire detection. For each pixel, a Kalman filter (KF) is used to blend the set of basis DTCs with the actual geostationary observations during times of confirmed cloud- and fire-free measurements, allowing for estimates of the pixels true non-fire ‘background’ signal to be provided throughout the day, even when a fire maybe present. This is different to the standard ‘spatial contextual’ approach, where the non-fire background signal is estimated from nearby non-fire pixels. A series of spectral thresholds are then applied to the analyzed pixel in order to identify whether the actual observation departs sufficiently from the estimated non-fire signal to confidently suggest that the pixel contains an active fire. If it does, the difference between the fire pixel and non-fire background signal estimate in the MIR is used to estimate the fire radiative power (FRP) output. We apply this new ‘Kalman Filter Algorithm’ (KFA) to one month of African imagery acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI), which is carried onboard the geostationary Meteosat satellite. We compare the resulting active fire detections and FRPs to those produced using the prototype (offline) version of the ‘spatial contextual’ based ‘Fire Thermal Anomalies’ (FTA) algorithm, here termed the p FTA, now used to generate the operational SEVIRI FRP-PIXEL product in the EUMETSAT Land Surface Analysis Satellite Applications Facility ( http://landsaf.meteo.pt/ ), and also compare results to simultaneous detections from the MODIS MOD14/MYD14 active fire products. The KFA shows some advantages over the p FTA algorithm, detecting a greater number of fire pixels, up to ~ 80% more at the peak of the diurnal fire cycle. These additional fire pixels are primarily low FRP fires ( p FTA algorithm, though this is still a substantial difference. Comparison against simultaneous MODIS active fire observations confirms that the KFA detects more of the MODIS-detected active fires than does the p FTA (60% more), but at the expense of doubling the false alarm rate. Analysis of the ability of the KFA to aid in the estimation of FRP by providing a more certain estimate of the fire pixels background signal indicates a small 0.2 K reduction (rmsd) between the ‘estimate’ of the background temperature and the ‘truth’. One limitation of the KFA approach currently is that it is computationally costly, and also requires the full day diurnal variation to be measured prior to the multi-temporal fire detection process. In its present form it is therefore unsuited to the generation of real-time products. We recommend future work concentrate on extracting maximum performance and utility of geostationary active fire observations by blending both ‘spatial contextual’ and ‘multi-temporal’ approaches.