Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1395-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1395-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
15 Mar 2024
Data description article |
| 15 Mar 2024
| 15 Mar 2024
Systematically tracking the hourly progression of large wildfires using GOES satellite observations
Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
James T. Randerson
Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
Yang Chen
Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
Douglas C. Morton
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Elizabeth B. Wiggins
Science Directorate, NASA Langley Research Center, Hampton, VA, USA
Padhraic Smyth
Department of Computer Science, University of California, Irvine, Irvine, CA, USA
Efi Foufoula-Georgiou
Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
Roy Nadler
Google, Mountain View, CA, USA
Omer Nevo
Google, Mountain View, CA, USA
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Total article views: 5,012 (including HTML, PDF, and XML)
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Total article views: 2,193 (including HTML, PDF, and XML)
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Cited
18 citations as recorded by crossref.
- Remote Sensing Techniques Shaping the Future of Wildfire Early Detection M. Raza et al. https://doi.org/10.1002/widm.70094
- Satellite-based analysis of hourly progression and driving factors of large U.S. wildfires S. Fang et al. https://doi.org/10.1016/j.jag.2026.105288
- A Comprehensive Survey of Satellite-Based Wildfire Indicators and Spatiotemporal Modeling Approaches: Past, Present, and Future S. Nurdiati et al. https://doi.org/10.3390/earth7040121
- Integrating earth observation data into the tri-environmental evaluation of the economic cost of natural disasters: a case study of 2025 LA wildfire Z. Li et al. https://doi.org/10.1016/j.jag.2026.105342
- Deep Learning for Downscaling Geostationary Satellite Measurements for Wildfire Detection L. Taulbee et al. https://doi.org/10.1109/TGRS.2026.3690311
- Near real-time indicators of burn severity in the western U.S. from active fire tracking E. Orland et al. https://doi.org/10.1186/s42408-025-00407-x
- Modeling Multi-Sensor Daily Fire Events in Brazil: The DescrEVE Relational Framework for Wildfire Monitoring H. Bernini et al. https://doi.org/10.3390/rs18040606
- Spatiotemporal Dynamics of Wildfire on Cyanobacterial Harmful Algal Blooms Proliferation A. Coker et al. https://doi.org/10.1021/acsestwater.6c00049
- GC Insights: Consistency in pyrocartography starts with color B. Hatchett https://doi.org/10.5194/gc-8-167-2025
- Characterizing wildfire behavior with ECOSTRESS land surface temperature across four California case studies M. Rivera et al. https://doi.org/10.1016/j.ecoinf.2026.103777
- Satellites in addressing climate change: Trends, challenges, and future directions G. Zhou et al. https://doi.org/10.1007/s42524-026-5183-6
- Satellite-borne identification and quantification of wildfire smoke emissions in North America via a novel UV-based index Z. Suo et al. https://doi.org/10.1016/j.atmosenv.2025.121069
- Unsupervised Wildfire Detection Using Multispectral MTG-FCI Data: A Feasibility Study A. Mercatini & N. Tartaglione https://doi.org/10.3390/jimaging12060229
- Leveraging additional VIIRS information to improve wildfire tracking in the western US S. Coffield et al. https://doi.org/10.1016/j.rse.2025.115156
- Extreme Colorado 2020 fires: remotely sensed burn severity influenced by treatments, forest types, and days of burning C. Stevens-Rumann et al. https://doi.org/10.1139/cjfr-2024-0329
- Multi-ignition fire complexes drive extreme fire years and impacts R. Scholten et al. https://doi.org/10.1126/sciadv.adx6477
- Quantitative framework for soil burn severity from numerical wildfire models H. Vahdat-Aboueshagh & S. McKenna https://doi.org/10.1016/j.envsoft.2025.106552
- A Review of interactive smoldering-flaming fires at the wildland-urban interface X. Huang et al. https://doi.org/10.1016/j.proci.2026.106298
18 citations as recorded by crossref.
- Remote Sensing Techniques Shaping the Future of Wildfire Early Detection M. Raza et al. https://doi.org/10.1002/widm.70094
- Satellite-based analysis of hourly progression and driving factors of large U.S. wildfires S. Fang et al. https://doi.org/10.1016/j.jag.2026.105288
- A Comprehensive Survey of Satellite-Based Wildfire Indicators and Spatiotemporal Modeling Approaches: Past, Present, and Future S. Nurdiati et al. https://doi.org/10.3390/earth7040121
- Integrating earth observation data into the tri-environmental evaluation of the economic cost of natural disasters: a case study of 2025 LA wildfire Z. Li et al. https://doi.org/10.1016/j.jag.2026.105342
- Deep Learning for Downscaling Geostationary Satellite Measurements for Wildfire Detection L. Taulbee et al. https://doi.org/10.1109/TGRS.2026.3690311
- Near real-time indicators of burn severity in the western U.S. from active fire tracking E. Orland et al. https://doi.org/10.1186/s42408-025-00407-x
- Modeling Multi-Sensor Daily Fire Events in Brazil: The DescrEVE Relational Framework for Wildfire Monitoring H. Bernini et al. https://doi.org/10.3390/rs18040606
- Spatiotemporal Dynamics of Wildfire on Cyanobacterial Harmful Algal Blooms Proliferation A. Coker et al. https://doi.org/10.1021/acsestwater.6c00049
- GC Insights: Consistency in pyrocartography starts with color B. Hatchett https://doi.org/10.5194/gc-8-167-2025
- Characterizing wildfire behavior with ECOSTRESS land surface temperature across four California case studies M. Rivera et al. https://doi.org/10.1016/j.ecoinf.2026.103777
- Satellites in addressing climate change: Trends, challenges, and future directions G. Zhou et al. https://doi.org/10.1007/s42524-026-5183-6
- Satellite-borne identification and quantification of wildfire smoke emissions in North America via a novel UV-based index Z. Suo et al. https://doi.org/10.1016/j.atmosenv.2025.121069
- Unsupervised Wildfire Detection Using Multispectral MTG-FCI Data: A Feasibility Study A. Mercatini & N. Tartaglione https://doi.org/10.3390/jimaging12060229
- Leveraging additional VIIRS information to improve wildfire tracking in the western US S. Coffield et al. https://doi.org/10.1016/j.rse.2025.115156
- Extreme Colorado 2020 fires: remotely sensed burn severity influenced by treatments, forest types, and days of burning C. Stevens-Rumann et al. https://doi.org/10.1139/cjfr-2024-0329
- Multi-ignition fire complexes drive extreme fire years and impacts R. Scholten et al. https://doi.org/10.1126/sciadv.adx6477
- Quantitative framework for soil burn severity from numerical wildfire models H. Vahdat-Aboueshagh & S. McKenna https://doi.org/10.1016/j.envsoft.2025.106552
- A Review of interactive smoldering-flaming fires at the wildland-urban interface X. Huang et al. https://doi.org/10.1016/j.proci.2026.106298
Saved (final revised paper)
Latest update: 21 Aug 2026
Short summary
To improve our understanding of extreme wildfire behavior, we use geostationary satellite data to develop the GOFER algorithm and track the hourly fire progression of large wildfires. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery and reveals considerable variability in fire spread rates on diurnal timescales. We create a product of hourly fire perimeters, active-fire lines, and fire spread rates for 28 fires in California.
To improve our understanding of extreme wildfire behavior, we use geostationary satellite data...
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