Sentinel-3 SLSTR active fire detection and FRP product: Pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets

Abstract

The Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view Earth observing instrument that builds on the heritage of the Along Track Scanning Radiometer (ATSR) series. SLSTR is planned for launch in 2013 on Sentinel-3, with two concurrently operating satellites planned for daily global coverage. Here we review the aspects of the SLSTR instrument design specifically targeted at biomass burning events, including operation of the SWIR channels at night and the inclusion of low-gain middle infrared and thermal IR channels that will minimise saturation over even high intensity fires. We detail the active fire detection and fire radiative power dataset to be provided from these SLSTR observations, which will be part of the SLSTR land product suite aimed at supporting both Global Monitoring for Environment and Security (GMES) operational services and scientific applications. We describe in detail the pre-launch active fire product algorithm, which uses data from the SLSTR near-nadir scan. The algorithm detects pixels containing actively burning fires, and uses the MIR radiance method to estimate their fire radiative power (FRP). We test the algorithm using a series of EOS MODIS scenes covering a range of fire-affected forest and savannah environments, comparing performance to that of the existing MODIS MOD14 ‘Fire and Thermal Anomaly’ products. Across 385 scenes covering Africa, South America and Australia, we find that the SLSTR algorithm applied to MODIS data detects in total 20% more fire pixels than does the MOD14 algorithm applied to the same data. Some scenes show very large differences, while others showed no differences, and some of the extra detections made by SLSTR maybe false alarms. For a better evaluation, we use the simultaneous high spatial resolution active fire detections made from ASTER to provide an independent accuracy assessment. Across 45 separate geographical regions covered simultaneously by ASTER and MODIS, we find that the SLSTR algorithm in fact detects 13% more correctly identified clusters of active fire pixels than the MOD14 algorithm, and that these contain 36% more active fire pixels. In particular, the SLSTR algorithm shows increased detection probabilities at small/low FRP fires, mainly due to the more liberal characteristics of its potential fire pixel detection stage. This performance enhancement comes, however, at the expense of a small (<2%) increase in commission error (i.e. false alarm rate) when compared to MOD14. The SLSTR algorithms ability to better detect low FRP fires maybe important, since these are usually the most common component of a region's fire regime.