The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. Current datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires. Global products: FireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011. FireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016. FireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019. FireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017. Continental products: FireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa.

FireMAFS was led by Prof Martin Wooster (Kings College, London) as part of QUEST Theme 3 (Quantifying and Understanding the Earth System) project. The objective of FireMAFS was to resolve limitations of fire modelling by developing a robust method to forecast fire activity (fire 'danger' indices, ignition probabilities, burnt area, fire intensity etc), via a process-based model of fire-vegetation interactions, tested, improved, and constrained. This used a state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. This dataset contains the MODIS Land Cover Type product multiple classification schemes, which describe land cover properties derived from observations spanning a year’s input of Terra and Aqua data. The data are stored in a 10 arc minute grid.

FireMAFS was led by Prof Martin Wooster (Kings College, London) as part of QUEST Theme 3 (Quantifying and Understanding the Earth System) project. This dataset collection contains the MODIS Land Cover Type product multiple classification schemes, which describe land cover properties derived from observations spanning a year’s input of Terra and Aqua data. The data are stored in a 10 arc minute grid. Fire was the most important disturbance agent worldwide in terms of area and variety of biomass affected, a major mechanism by which carbon is transferred from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Despite such clear coupling between fire, climate, and vegetation, fire was not modelled as an interactive component of the climate/earth systems models of full complexity or intermediate complexity, that are used to model terrestrial ecosystem processes principally for simulating CO2 exchanges. The objective of FireMAFS was to resolve these limitations by developing a robust method to forecast fire activity (fire 'danger' indices, ignition probabilities, burnt area, fire intensity etc), via a process-based model of fire-vegetation interactions, tested, improved, and constrained. This used a state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Much of the activity of FireMAFS was shaped by the research and technical priorities of QUESTESM (earth system model). Key activities included the progressive development of the JULES-ED and SPITFIRE submodels. Fire is now very well represented in QESM (Quest Earth System Model), making progress towards a modelling capability for fire risk forecasting in the context of global change.