Earth Observations of Pre-Fire Fuel Conditions for the Prediction of Large-Scale Wildfires: Building Up Operational Systems
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 37
Special Issue Editor
Interests: satellite-based earth observation; vegetation moisture; soil moisture; drought/wildfires prediction; data assimilation; vegetation dynamics; passive microwave sensors; synthetic aperture radar; bias correction; heatwaves
Special Issue Information
Dear Colleagues,
As wildfires, drought, and extreme heat waves become more frequent and more severe, forests and agricultural lands are turning from carbon sinks into super‑emitters. In this regard, it is necessary to monitor whether our forests and vegetated lands are evolving into heat storage (as fuels) or carbon sinks to enhance fire predictions, to renovate carbon policies, and to establish timely measures ahead of disasters. However, unfortunately, Near-Real-Time (NRT) large-scale (e.g., synoptic or mesoscale) observations for vegetation moisture or fuel temperature over vegetated lands including forests, grasses, and shrubs remain unavailable at an operational level, although there is still considerable reliance on weather information such as wind, air temperature, or humidity. Even though studies on fuel moisture have been presented, the validation of fuel moisture or vegetation heat over large-scale wildfire spread or burn areas is rare. Thus, we continue making advancements toward a new path for the monitoring and prediction of large-scale wildfires. Traditionally, some vegetation-related variables such as vegetation moisture are often retrieved from thermal or optical multispectral sensors through land surface temperature or Vegetation Index products. However, there is significant reliance on empirical or local approaches for monitoring hydrological or thermal changes, which make it difficult to transition to an operational system.
This Special Issue aims at promoting and supporting innovative studies, for example, that develop new sensors that have never been applied before, pioneer new retrieval algorithms, explore unknown mechanisms, or overcome limitations in existing retrieval algorithms for the estimation of fuel conditions or the prediction of fuel-driven wildfires. Articles may address, but are not limited, to the following topics:
- Biomass (as fuels) estimation;
- Forest fuel condition mapping and pattern analysis;
- Carbon cycle/sequestration/emission related to wildfires, harvesting and grazing;
- Vegetation water contents;
- Live fuel moisture contents;
- Fuel (vegetation) temperature;
- Dry matter contents;
- Radiative Transfer Models for forest vegetation dynamics;
- Wildfire prediction/risk assessment;
- Land-driven ignition;
- Fire spread rates/fire severity;
- Burned areas;
- L-band, S-band synthetic aperture radar (SAR), interferometric radiometer;
- Large-scale land surface-driven wildfires;
- Validation over large-scale wildfires.
Dr. Ju Hyoung Lee
Guest Editor
Manuscript Submission Information
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Keywords
- large-scale wildfires
- microwave sensors
- fuel conditions
- forest temperature
- vegetation moisture
- fire severity
- fire prediction
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