AbstractThe gradual increase in Landsat-class data availability creates new opportunities for fire science and management applications that require higher-fidelity information about biomass burning, improving upon existing coarser spatial resolution (≥1km) satellite active fire data sets. Targeting those enhanced capabilities we describe an active fire detection algorithm for use with Landsat-8 Operational Land Imager (OLI) daytime and nighttime data. The approach builds on the fire-sensitive short-wave infrared channel 7 complemented by visible and near-infrared channel 1–6 data (daytime only), while also expanding on the use of multi-temporal analysis to improve pixel classification results. Despite frequent saturation of OLI's fire-affected pixels, which includes radiometric artifacts resulting from folding of digital numbers, our initial assessment based on visual image analysis indicated high algorithm fidelity across a wide range of biomass burning scenarios, gas flares and active volcanoes. Additional field data verification confirmed the sensor's and algorithm's ability to resolve fires of significantly small areas compared to current operational satellite fire products. Commission errors were greatly reduced with the addition of multi-temporal analysis tests applied to co-located pixels, averaging less than 0.2% globally. Because of its overall quality, Landsat-8/OLI active fire data could become part of a network of emerging earth observation systems providing enhanced spatial and temporal coverage of biomass burning at global scales.