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| MODIS Active Fire | |
|---|---|
| Name | MODIS Active Fire |
| Mission | Terra and Aqua |
| Operator | NASA / National Oceanic and Atmospheric Administration |
| Instrument | MODIS |
| Launch | Terra (1999), Aqua (2002) |
| Type | Active fire detection |
MODIS Active Fire MODIS Active Fire is a global satellite-based fire detection dataset derived from the Moderate Resolution Imaging Spectroradiometer aboard Terra and Aqua. It provides near-real-time identification and characterization of thermal anomalies to support disaster response, environmental monitoring, and scientific research across regions including Amazon Rainforest, Siberia, and California. Developed and maintained by teams linked to NASA, the product feeds operational systems used by agencies such as the U.S. Forest Service, European Space Agency, and Australian Bureau of Meteorology.
MODIS Active Fire supplies geolocated thermal anomaly detections (commonly called "hotspots") with attributes such as confidence, radiative power, and time of observation. The dataset complements other remote sensing resources such as Landsat, Sentinel-2, and VIIRS by offering high temporal revisit and broad spatial coverage over continents, oceans, and polar regions including the Arctic and Antarctica. Analysts from institutions like University of Maryland, University of California, Berkeley, CSIRO, and Max Planck Institute for Meteorology routinely integrate MODIS detections into studies of biomass burning, emissions inventories, and land-use change across areas like the Cerrado, Boreal forests, and Mediterranean Basin.
The detection algorithm uses multispectral thresholding primarily in the mid-infrared bands of the MODIS instrument, leveraging contrasts between thermal bands and neighboring reflective bands to distinguish fires from false positives such as volcanoes and industrial heat sources like those in Sakhalin Oblast or Khanty-Mansi Autonomous Okrug. Algorithm development involved collaboration among researchers at NASA Goddard Space Flight Center, Jet Propulsion Laboratory, and the University of Maryland. Temporal contextual tests and contextual background estimation reduce detections from sun glint over features such as Lake Victoria and Persian Gulf. Confidence metrics are informed by comparisons with ground campaigns conducted in regions including Amazonas (Brazilian state), Sumatra, and British Columbia.
Products are distributed as point-level hotspot tables, gridded burned area overlays, and ancillary metadata in formats like GeoTIFF, KML, and binary HDF-EOS used by projects at Oak Ridge National Laboratory and National Center for Atmospheric Research. Output fields include geodetic coordinates, detection time (UTC), brightness temperature, and Fire Radiative Power (FRP), a metric used by researchers at European Commission Joint Research Centre, NOAA National Centers for Environmental Information, and NASA Langley Research Center for emissions estimation and smoke plume modeling related to events such as the 2019–20 Australian bushfire season and the 2019 Amazon rainforest wildfires.
Validation efforts compare MODIS hotspots with in situ fire observations from agencies like the U.S. Geological Survey, prescribed burn records from the Bureau of Land Management, and airborne campaigns by NOAA and NASA ER-2 flights. Accuracy assessments involve cross-comparison with higher-resolution sensors such as Landsat 8, Sentinel-2, and Planet Labs imagery; studies at institutions including Columbia University and University of Exeter quantify commission and omission errors across ecosystems like the Congo Basin and Chilean temperate rainforests. Radiative power calibration draws on community intercomparisons coordinated with Global Fire Emissions Database contributors and atmospheric chemistry groups at Max Planck Institute for Chemistry.
Users apply MODIS Active Fire to emergency response by agencies such as Federal Emergency Management Agency and Copernicus Emergency Management Service, to air quality forecasting by organizations like European Centre for Medium-Range Weather Forecasts and AirNow, and to carbon emissions accounting by programs including Global Carbon Project and Intergovernmental Panel on Climate Change assessments. Ecologists at Yale University, Smithsonian Institution, and Woods Hole Research Center use detections to study successional dynamics in places like Serengeti and Pantanal. Agricultural monitoring by entities such as Food and Agriculture Organization and World Bank leverages hotspot time series for landscape fire management and post-fire recovery assessments.
MODIS Active Fire faces spatial resolution limits (nominal 1 km fire pixel) that constrain detection of small or understory fires in complex terrains like the Himalayas or urban-wildland interfaces near Los Angeles. Cloud cover, smoke plumes, and sun glint can obscure thermal signals over regions such as Amazon Basin and Central Africa, while mixed-pixel effects complicate attribution in mosaicked landscapes like Mekong Delta. Temporal sampling leads to missed transient events between overpasses of Terra and Aqua, prompting integration with geostationary platforms such as GOES-R and higher-resolution polar sensors like VIIRS.
MODIS Active Fire datasets are accessible through NASA data services and portals used by USGS Earth Explorer, NASA Earthdata, and the Hawaii Institute of Geophysics and Planetology mirror sites, and are supported in analysis environments like Google Earth Engine, QGIS, and ArcGIS. Open-source libraries and toolkits developed by groups at University of Oxford and CSU enable ingestion, visualization, and fusion with air quality models such as those from GEOS-Chem and tools used by Sentinel Hub.