Articles | Volume 21, issue 1
https://doi.org/10.5194/bg-21-279-2024
https://doi.org/10.5194/bg-21-279-2024
Research article
 | 
17 Jan 2024
Research article |  | 17 Jan 2024

A global fuel characteristic model and dataset for wildfire prediction

Joe R. McNorton and Francesca Di Giuseppe

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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.
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