Articles | Volume 20, issue 4
https://doi.org/10.5194/nhess-20-1097-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-1097-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Satellite hydrology observations as operational indicators of forecasted fire danger across the contiguous United States
Alireza Farahmand
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
E. Natasha Stavros
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Ali Behrangi
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
James T. Randerson
Department of Earth System Science, University of California, Irvine, CA, USA
Brad Quayle
Geospatial Technology and Applications Center, USDA Forest Service, 2222 West 2300 South, Salt Lake City, UT, USA
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Cited
13 citations as recorded by crossref.
- Wildfire Trend Analysis over the Contiguous United States Using Remote Sensing Observations J. Salguero et al. 10.3390/rs12162565
- 2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities M. Ehsani et al. 10.3390/w12113067
- The Spatiotemporal Characteristics of Wildfires across Australia and Their Connections to Extreme Climate Based on a Combined Hydrological Drought Index L. Cui et al. 10.3390/fire6020042
- A holistic overview of the applications of GRACE-observed terrestrial water storage in hydrology and climate science B. Khorrami & O. Gündüz 10.1007/s10661-025-14207-y
- Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation H. Filho et al. 10.3390/su14159419
- Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing E. Bousquet et al. 10.5194/bg-19-3317-2022
- Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models M. Pascolini-Campbell et al. 10.1038/s41598-025-06814-z
- Microwave Retrievals of Soil Moisture Improve Grassland Wildfire Predictions A. Rigden et al. 10.1029/2020GL091410
- Sub-hourly forecasting of fire potential using machine learning on time series of surface weather variables A. Ardid et al. 10.1071/WF24113
- Drought Cascade in the Terrestrial Water Cycle: Evidence From Remote Sensing A. Farahmand et al. 10.1029/2021GL093482
- Water cycle science enabled by the GRACE and GRACE-FO satellite missions M. Rodell & J. Reager 10.1038/s44221-022-00005-0
- Boots on the Ground and Eyes in the Sky: A Perspective on Estimating Fire Danger from Soil Moisture Content S. Sharma & K. Dhakal 10.3390/fire4030045
- Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States A. Farahmand et al. 10.3390/rs12081252
12 citations as recorded by crossref.
- Wildfire Trend Analysis over the Contiguous United States Using Remote Sensing Observations J. Salguero et al. 10.3390/rs12162565
- 2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities M. Ehsani et al. 10.3390/w12113067
- The Spatiotemporal Characteristics of Wildfires across Australia and Their Connections to Extreme Climate Based on a Combined Hydrological Drought Index L. Cui et al. 10.3390/fire6020042
- A holistic overview of the applications of GRACE-observed terrestrial water storage in hydrology and climate science B. Khorrami & O. Gündüz 10.1007/s10661-025-14207-y
- Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation H. Filho et al. 10.3390/su14159419
- Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing E. Bousquet et al. 10.5194/bg-19-3317-2022
- Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models M. Pascolini-Campbell et al. 10.1038/s41598-025-06814-z
- Microwave Retrievals of Soil Moisture Improve Grassland Wildfire Predictions A. Rigden et al. 10.1029/2020GL091410
- Sub-hourly forecasting of fire potential using machine learning on time series of surface weather variables A. Ardid et al. 10.1071/WF24113
- Drought Cascade in the Terrestrial Water Cycle: Evidence From Remote Sensing A. Farahmand et al. 10.1029/2021GL093482
- Water cycle science enabled by the GRACE and GRACE-FO satellite missions M. Rodell & J. Reager 10.1038/s44221-022-00005-0
- Boots on the Ground and Eyes in the Sky: A Perspective on Estimating Fire Danger from Soil Moisture Content S. Sharma & K. Dhakal 10.3390/fire4030045
Latest update: 31 Jul 2025
Short summary
Wildfires result in billions of dollars of losses each year. Most wildfire predictions have a 10 d lead-time. This study introduces a framework for a 1-month lead-time prediction of wildfires based on vapor pressure deficit and surface soil moisture in the US. The results show that the model can successfully predict burned area with relatively small margins of error. This is especially important for operational wildfire management such as national resource allocation.
Wildfires result in billions of dollars of losses each year. Most wildfire predictions have a...
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