Preprints
https://doi.org/10.5194/egusphere-2026-89
https://doi.org/10.5194/egusphere-2026-89
10 Mar 2026
 | 10 Mar 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Observation operator and detection limits for MODIS and VIIRS Fire Radiative Power products

Mikhail Sofiev and Rostislav Kouznetsov

Abstract. We analyze the MODIS and VIIRS active-fire Fire Radiative Power (FRP) products and suggest an approximation for the FRP detection limit as a linear function of a pixel area and develop analytical observation operators for both pixel-level and gridded FRP products. We have shown that the collection-6.1 of MODIS has the FRP detection limit at 3.7 MW in case of a nadir retrieval at a clear-sky night. For a daytime sideview, it reaches as high as 40 MW. For VIIRS, the smallest fire reliably detected at night is 0.5 MW. Application of the developed operators for the cross-mapping the MODIS and VIIRS datasets at the pixel/granule level confirmed their consistency. Applying the grid-level operators alongside satellite overpass and weather data, we generated global maps of fire detection probabilities, revealing regional variations. As a global average, probability of detection of powerful fires is 78 % for MODIS (88 % for VIIRS). Variations between regions are large: in some equatorial areas this probability is less than 30 % for MODIS (40% for VIIRS).

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Mikhail Sofiev and Rostislav Kouznetsov

Status: open (until 06 May 2026)

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Mikhail Sofiev and Rostislav Kouznetsov
Mikhail Sofiev and Rostislav Kouznetsov

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Short summary
Satellites can observe fires globally, but sensitivity of instruments in space is limited: they miss small fires and cannot see through clouds. We analyzed the MODIS and VIIRS fire observations and obtained their sensitivity to small fires, which depends on the resolution of the instrument, timing of the observation (day or night), and details of the data processing. We developed a procedure for comparing fire model predictions with satellite observations accounting for their limited sensitivity
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