VIIRS Nightfire Version 4.0 Now Operational Across Suomi NPP, NOAA-20, and NOAA-21 satellites
VIIRS Nightfire Version 4.0 Now Operational Across Suomi NPP, NOAA-20, and NOAA-21 satellites
PAYNE INSTITUTE COMMENTARY SERIES: COMMENTARY
July 2, 2025
New version of VIIRS Nightfire (VNF) 4.0 [1] has significant improvements in the following areas As of July 1, 2025, the new version of the VIIRS Nightfire (VNF) algorithm Version 4.0 [1] is available for all three VIIRS satellites: Suomi NPP, NOAA-20, and NOAA-21. This release marks a significant advance in nighttime thermal anomaly detection and characterization, offering researchers and practitioners enhanced capabilities across a wide range of applications, from wildfire monitoring to industrial flaring assessment. Key improvements in VNF 4.0 include:
1. Enhanced Sensitivity and Noise Filtering:
The detection algorithms for independent heat sources have been refined in the SWIR bands M10, M11 [2] and the MWIR bands M12–M13 [3], improving sensitivity to smaller and lower-intensity sources while reducing false detections.
2. Atmospheric Correction:
Version 4.0 incorporates correction for atmospheric transmittance, enabling retrieval of heat source parameters such as temperature, source area, and radiative heat at the Earth’s surface. Previous VNF versions [4] estimated these parameters only at the top of the atmosphere.
3. Advanced Spectral Unmixing:
The new version supports decomposition of mixed pixels into three components: a high-temperature source, a mid-temperature source, and background. Earlier releases could only separate a single high-temperature source from background. This enhancement is particularly valuable for analyzing wildfires with multiple combustion phases or industrial complexes combining gas flaring and other thermal processes, such as catalytic cracking.
4. Improved Filtering of Non-Anthropogenic Noise:
Enhanced noise filtering more effectively excludes non-target signals, such as auroral emissions, which can be especially problematic during periods of elevated solar activity.
5. Gas Flowrate Estimation:
VNF 4.0 reports gas flaring flow rates for known sites listed in the multiyear infrared emitter catalog [5], providing near-real time data on the ecological impact of oil and gas exploration.
6. Optimized Computational Performance:
The algorithm has been re-engineered for efficient near-real-time processing. Unlike earlier versions requiring a supercomputing cluster at the Colorado School of Mines, VNF 4.0 processes a full day of data from all three satellites in under two hours on a single high-performance server.
An experimental version of VNF 4.0 has been in continuous operational testing by EOG for Suomi NPP data since September 2023, with only interruption in late 2024 due to gaps in atmospheric aerosol input data.
Starting July 1, 2025, VNF 4.0 output products including CSV tables and KML maps with detections and satellite data coverage are available for download by registered users and API clients at:
https://eogdata.mines.edu/products/vnf/vnf_v40.html.
References
[1] Elvidge, C. D., Zhizhin, M., Hsu, F. C., Sparks, T., & Ghosh, T. (2021). Subpixel Analysis of Primary and Secondary Infrared Emitters with Nighttime VIIRS Data. Fire, 4(4), 83.
[2] Elvidge, C. D., Zhizhin, M., Baugh, K., Hsu, F. C., & Ghosh, T. (2019). Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data. Remote Sensing, 11(4), 395.
[3] Zhizhin, M., Elvidge, C., & Poyda, A. (2023). Night-Time Detection of Subpixel Emitters with VIIRS Mid-Wave Infrared Bands M12–M13. Remote Sensing, 15(5), 1189. https://doi.org/10.3390/rs15051189
[4] Elvidge, C. D., Zhizhin, M., Hsu, F.-C., & Baugh, K. E. (2013). VIIRS Nightfire: Satellite Pyrometry at Night. Remote Sensing, 5(9), 4423–4449.
[5] Elvidge, C. D., Zhizhin, M., Sparks, T., Ghosh, T., Pon, S., Bazilian, M., Sutton, P. C., & Miller, S. D. (2023). Global Satellite Monitoring of Exothermic Industrial Activity via Infrared Emissions. Remote Sensing, 15(19), 4760. https://doi.org/10.3390/rs15194760
ABOUT THE AUTHORS
Mikhail Zhizhin,
Research Associate, Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines
Mikhail Zhizhin, M.Science in mathematics from the Moscow State University in 1984, Ph.D. in computational seismology and pattern recognition from the Russian Acad. Sci. in 1992. Research positions from 1987 to 2012 in geophysics, space research and nuclear physics at Russian Acad. Sci., later at NOAA and CU Boulder. Currently he is a researcher at the Earth Observation Group at Colorado School of Mines. His applied research fields evolved from high performance computing in seismology, geodynamics, terrestrial and space weather to deep learning in remote sensing. He is developing new machine learning algorithms to better understand the Nature with Big Data.
Christopher Elvidge
Senior Research Associate, Director of Earth Observation Group
Christopher D. Elvidge has decades of experience with satellite low-light imaging data, starting in 1994. He pioneered nighttime satellite observation on visible lights, heat sources including gas flares and wildfires, as well as bright lit fishing vessels. He led the development of these nighttime remote sensed products with images from DMSP, JPSS, and Landsat satellites. These data are very popular and used globally in both public and private sectors. As of February 2018, he has more than 11,000 scholarly publication citations.
Morgan Bazilian
Director, Payne Institute and Professor of Public Policy
Morgan Bazilian is the Director of the Payne Institute and a Professor of public policy at the Colorado School of Mines. Previously, he wD.as lead energy specialist at the World Bank. He has over two decades of experience in the energy sector and is regarded as a leading expert in international affairs, policy and investment. He is a Member of the Council on Foreign Relations.
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