Seeing fire from space
When a wildfire burns, it radiates energy across the electromagnetic spectrum. Most of that radiation is in the thermal infrared — wavelengths that human eyes cannot see but satellite sensors can detect clearly even from 700 kilometers above the Earth. This is the physical basis for all satellite fire detection: fires are hotter than their surroundings, and that temperature contrast is visible in specific infrared bands.
Two instrument families dominate global fire monitoring: MODIS (Moderate Resolution Imaging Spectroradiometer) aboard NASA's Terra and Aqua satellites, and VIIRS (Visible Infrared Imaging Radiometer Suite) aboard the Suomi NPP and NOAA-20 satellites. Together, they provide multiple daily observations of every point on Earth's surface.
How MODIS detects fires
MODIS has been operational since 2000 on Terra and 2002 on Aqua. Its fire detection algorithm uses two thermal infrared channels centered at 4 micrometers and 11 micrometers. The 4-micrometer channel is particularly sensitive to hot targets because thermal emission from fires peaks near this wavelength, while the cooler background landscape emits more strongly at longer wavelengths.
The algorithm compares each pixel's brightness temperature in the 4-micrometer band against its neighbors and against expected background values. If the pixel is significantly hotter than its context — indicating an active fire or very hot surface — it is flagged as a fire detection. Additional tests reject false positives from sun glint, hot desert surfaces, and industrial heat sources.
- Spatial resolution: 1 km at nadir (each pixel covers roughly 1 square kilometer)
- Revisit time: each satellite passes a given location approximately every 1–2 days
- Combined Terra + Aqua: typically 4 overpasses per day at mid-latitudes
- Fire detection confidence: classified as low, nominal, or high
VIIRS: the next generation
VIIRS, first launched in 2011, provides significantly improved fire detection compared to MODIS. Its I-band (375-meter resolution) fire product detects smaller fires and provides better spatial precision for mapping fire perimeters. A fire that occupies only a fraction of a MODIS pixel might fill a larger fraction of a VIIRS pixel, making it detectable at lower intensities.
VIIRS also has a day-night band that can detect fires at night using their visible light emission. This is useful for verifying fire detections and for estimating fire intensity from the brightness of the flames themselves, independent of thermal infrared measurements.
Fire radiative power: measuring how much energy a fire releases
Beyond simply detecting the presence of fire, both MODIS and VIIRS estimate fire radiative power (FRP) — the rate of radiant energy emission from the fire at the moment of satellite overpass, measured in megawatts. FRP is directly related to the rate of fuel combustion: a fire burning more biomass per unit time emits more radiant energy.
FRP measurements allow scientists to estimate emissions of smoke, CO2, and particulate matter. They also help emergency managers prioritize: a cluster of detections with high FRP is likely a large, intensely burning fire, while low-FRP detections may indicate smoldering or residual heat. PlanetSentry can display FRP values when available to help users assess fire intensity at a glance.
The data pipeline: from satellite to FIRMS to your screen
Raw satellite data is downlinked to ground stations, processed through NASA's direct broadcast and central processing facilities, and made available through the Fire Information for Resource Management System (FIRMS). FIRMS provides near-real-time fire data with a typical latency of 3–4 hours from satellite observation to data availability.
The data is distributed as CSV, shapefile, KML, and WMS layers. Monitoring platforms like PlanetSentry can fetch active fire data from FIRMS APIs, convert it to GeoJSON, and overlay it on the globe. The pipeline is: satellite overpass → ground station → NASA processing → FIRMS API → PlanetSentry display.
Limitations and false positives
Satellite fire detection is not perfect. Clouds block thermal infrared sensors completely — a fire burning beneath thick cloud cover is invisible to MODIS and VIIRS. Smoke can also partially obscure fire detections. This means satellite data consistently underestimates the total number and extent of active fires.
False positives occur from sun glint off reflective surfaces, hot industrial facilities, volcanic thermal anomalies, and occasionally from warm bare soil in desert environments. Detection algorithms include filters for many of these sources, but some false positives persist, particularly at low confidence levels.
- Cloud cover: the primary cause of missed fire detections globally
- Revisit gaps: fires that start and extinguish between overpasses may never be detected
- Small fires: both instruments have minimum detectable fire sizes that depend on fire temperature and contrast
- Timing: a satellite snapshot captures one moment — fire conditions change rapidly
Geostationary satellites: filling the temporal gaps
Polar-orbiting satellites like Terra, Aqua, and Suomi NPP see each location only a few times per day. Geostationary satellites like GOES (Americas), Himawari (Asia-Pacific), and Meteosat (Europe/Africa) orbit at 36,000 km altitude and image the same hemisphere continuously, providing fire detection updates every 10–15 minutes.
The tradeoff is spatial resolution: geostationary fire products have pixel sizes of 2–4 km, much coarser than VIIRS. But the temporal frequency is invaluable for tracking fire behavior in near-real-time. Rapidly intensifying fires, new ignitions, and fire-line movement become visible within minutes rather than hours.
Why multiple sensors matter
No single satellite sensor provides a complete picture of global fire activity. Each has gaps in coverage, timing, resolution, or sensitivity. The most accurate fire monitoring combines polar-orbiting high-resolution sensors (VIIRS, MODIS) for detection and characterization with geostationary sensors (GOES ABI, Himawari AHI) for temporal tracking.
This multi-sensor approach is why platforms that integrate multiple data sources — EONET event categories, FIRMS hotspot data, and weather satellite imagery — offer a more complete picture than any single feed. The goal is to close the observational gaps so that a fire detected by one sensor is confirmed, tracked, and contextualized by others.