Articles | Volume 21, issue 1
https://doi.org/10.5194/bg-21-279-2024
© 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/bg-21-279-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jan 2024
Research article |
| 17 Jan 2024
| 17 Jan 2024
A global fuel characteristic model and dataset for wildfire prediction
Joe R. McNorton
CORRESPONDING AUTHOR
Research Department, European Centre for Medium-Range Weather Forecasts, Reading, RG45AJ, UK
Francesca Di Giuseppe
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, RG45AJ, UK
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Cited
17 citations as recorded by crossref.
- Examining the Transferability of Remote-Sensing-Based Models of Live Fuel Moisture Content for Predicting Wildfire Characteristics E. Guk et al. 10.1109/JSTARS.2024.3445138
- Biomass burning emission estimation in the MODIS era: State-of-the-art and future directions M. Parrington et al. 10.1525/elementa.2024.00089
- Evaluating the performance of spectral indices and meteorological variables as indicators of live fuel moisture content in Mediterranean shrublands M. Alicia Arcos et al. 10.1016/j.ecolind.2024.112894
- Assessing fire danger classes and extreme thresholds of the Canadian Fire Weather Index across global environmental zones: a review L. Kudláčková et al. 10.1088/1748-9326/ad97cf
- Enhancing seasonal fire predictions with hybrid dynamical and random forest models M. Torres-Vázquez et al. 10.1038/s44304-025-00069-4
- Convective potential and fuel availability complement near-surface weather in regulating global wildfire activity H. Su et al. 10.1126/sciadv.adp7765
- State of Wildfires 2023–2024 M. Jones et al. 10.5194/essd-16-3601-2024
- Deep learning for wildfire risk prediction: Integrating remote sensing and environmental data Z. Xu et al. 10.1016/j.isprsjprs.2025.06.002
- Modeling Natural Forest Fire Regimes Based on Drought Characteristics at Various Spatial and Temporal Scales in P. R. China X. Shao et al. 10.3390/f16071041
- Prediction and key drivers analysis of forest surface Dead Fine Fuel Moisture Content: A stacking ensemble learning and IoT-based system Y. Li et al. 10.1016/j.indic.2025.100937
- Global data-driven prediction of fire activity F. Di Giuseppe et al. 10.1038/s41467-025-58097-7
- An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment N. Perello et al. 10.1016/j.envsoft.2024.106254
- Lightning-ignited wildfire prediction in the boreal forest of northeast China C. Gao et al. 10.1016/j.gloplacha.2025.104948
- Probability and spatiotemporal dynamics of active fire occurrence in Inner Mongolia, China from 2000 to 2022 X. Jia et al. 10.1007/s40333-025-0027-5
- A Near-Real-Time Operational Live Fuel Moisture Content (LFMC) Product to Support Decision-Making at the National Level A. Benali et al. 10.3390/fire8050178
- The global drivers of wildfire O. Haas et al. 10.3389/fenvs.2024.1438262
- A hybrid framework to estimate live fuel moisture content through land surface modelling F. Santos et al. 10.1007/s40808-025-02561-2
17 citations as recorded by crossref.
- Examining the Transferability of Remote-Sensing-Based Models of Live Fuel Moisture Content for Predicting Wildfire Characteristics E. Guk et al. 10.1109/JSTARS.2024.3445138
- Biomass burning emission estimation in the MODIS era: State-of-the-art and future directions M. Parrington et al. 10.1525/elementa.2024.00089
- Evaluating the performance of spectral indices and meteorological variables as indicators of live fuel moisture content in Mediterranean shrublands M. Alicia Arcos et al. 10.1016/j.ecolind.2024.112894
- Assessing fire danger classes and extreme thresholds of the Canadian Fire Weather Index across global environmental zones: a review L. Kudláčková et al. 10.1088/1748-9326/ad97cf
- Enhancing seasonal fire predictions with hybrid dynamical and random forest models M. Torres-Vázquez et al. 10.1038/s44304-025-00069-4
- Convective potential and fuel availability complement near-surface weather in regulating global wildfire activity H. Su et al. 10.1126/sciadv.adp7765
- State of Wildfires 2023–2024 M. Jones et al. 10.5194/essd-16-3601-2024
- Deep learning for wildfire risk prediction: Integrating remote sensing and environmental data Z. Xu et al. 10.1016/j.isprsjprs.2025.06.002
- Modeling Natural Forest Fire Regimes Based on Drought Characteristics at Various Spatial and Temporal Scales in P. R. China X. Shao et al. 10.3390/f16071041
- Prediction and key drivers analysis of forest surface Dead Fine Fuel Moisture Content: A stacking ensemble learning and IoT-based system Y. Li et al. 10.1016/j.indic.2025.100937
- Global data-driven prediction of fire activity F. Di Giuseppe et al. 10.1038/s41467-025-58097-7
- An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment N. Perello et al. 10.1016/j.envsoft.2024.106254
- Lightning-ignited wildfire prediction in the boreal forest of northeast China C. Gao et al. 10.1016/j.gloplacha.2025.104948
- Probability and spatiotemporal dynamics of active fire occurrence in Inner Mongolia, China from 2000 to 2022 X. Jia et al. 10.1007/s40333-025-0027-5
- A Near-Real-Time Operational Live Fuel Moisture Content (LFMC) Product to Support Decision-Making at the National Level A. Benali et al. 10.3390/fire8050178
- The global drivers of wildfire O. Haas et al. 10.3389/fenvs.2024.1438262
- A hybrid framework to estimate live fuel moisture content through land surface modelling F. Santos et al. 10.1007/s40808-025-02561-2
Latest update: 09 Oct 2025
Short summary
Wildfires have wide-ranging consequences for local communities, air quality and ecosystems. Vegetation amount and moisture state are key components to forecast wildfires. We developed a combined model and satellite framework to characterise vegetation, including the type of fuel, whether it is alive or dead, and its moisture content. The daily data is at high resolution globally (~9 km). Our characteristics correlate with active fire data and can inform fire danger and spread modelling efforts.
Wildfires have wide-ranging consequences for local communities, air quality and ecosystems....
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