Sentinel-2 MSI data for active fire detection in major fire-prone biomes: A multi-criteria approach

•Fully automatic algorithm based on adaptive thresholds for each biome class.•Good accuracy with a balanced amount of omission and commission errors.•Fast computation without the requirement of using multi-temporal imagery.•Suitable for future satellite on-board processors. Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of enhanced spatial and temporal coverage for monitoring biomass burning which could complement other coarse active fire detection products. This paper aims to investigate the use of reflective wavelength Sentinel-2 data to classify unambiguous active fire areas from inactive areas at 20 m spatial resolution. A multi-criteria approach based on the reflectance of several bands (i.e. B4, B11, and B12) is proposed to demonstrate the boundary constraints in several representative biomes. It is a fully automatic algorithm based on adaptive thresholds that are statistically determined from 11 million Sentinel-2 observations acquired over corresponding summertime (June 2019 to September 2019) across 14 regions or countries. Biome-based parameterizations avoid high omission errors (OE) caused by small and cool fires in different landscapes. It also takes advantage of the multiple criteria whose intersection could reduce the potential commission errors (CE) due to soil dominated pixels or highly reflective building rooftops. Active fire detection performance was mainly evaluated through visual inspection on eight illustrative subsets because of unavailable ground truth. The detection results revealed that CE and OE could be kept at a low level with 0.14 and 0.04 as an acceptable trade-off. The proposed algorithm can be employed for rapid active fire detection as soon as the image is obtained without the requirement of using multi-temporal imagery, and can even be adapted to onboard processing in the future.