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The Visible and Infra-Red Radiometer (VIRR) is an improved third-generation earth observation sensor that has channels ranging from the visible to thermal bands and is carried on board the Chinese FengYun-3C satellite. The VIRR instrument provides full global coverage at a spatial resolution of 1 km. It is neither appropriate nor correct to simply duplicate the parameters used in algorithms designed for other sensors. Hence, this paper proposes an active fire-monitoring algorithm designed for the VIRR sensor. A newly proposed concept—the “infrared channel slope”—is employed in the algorithm, which can detect active fire pixels by identifying deviations in their mid-infrared and thermal infrared brightness temperatures. “Dynamic thresholds” are also used to allow the algorithm to adapt to different situations. Different fire events that occurred in different parts of the world between 2014 and 2016 are selected to test the robustness of the algorithm. The testing consists of two parts: a rough assessment using products of a similar spatial resolution and then a detailed assessment based on fine spatial resolution images. In the rough assessment part, the results are compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Fire and Thermal Anomaly product (MOD14A1) and the China Meteorological Administration’s Global Fire References (GFR) products from the National Satellite Meteorological Center. In the detailed assessment, imagery acquired by the Landsat-7/Enhanced Thematic Mapper Plus and the Landsat-8/Operational Land Imager is used. The MODIS and Landsat data are utilized as reference data to validate the accuracy while the comparison with the GFR products is carried out to demonstrate the advantages of this method. The assessment demonstrates that the algorithm produces comparable results to those obtained using MOD14A1 and is a significant improvement compared with GFR.

Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager (MERSI) onboard FengYun-3C (FY-3C) has shown great potential for burned area mapping research, but there is still a lack of relevant studies and applications. This paper describes an automated burned area mapping algorithm that was developed using daily MERSI data. The algorithm employs time-series analysis and multi-temporal 1000-m resolution data to obtain seed pixels. To identify the burned pixels automatically, region growing and Support Vector Machine) methods have been used together with 250-m resolution data. The algorithm was tested by applying it in two experimental areas, and the accuracy of the results was evaluated by comparing them to reference burned area maps, which were interpreted manually using Landsat 8 OLI data and the MODIS MCD64A1 burned area product. The results demonstrated that the proposed algorithm was able to improve the burned area mapping accuracy at the two study sites.