2022
DOI: 10.3390/rs14122941
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Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Abstract: Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (A… Show more

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Cited by 4 publications
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“…The second direction taken is to combine highly simplified, empirical-or semi-empirical-based fire models with relatively complete numerical weather prediction models with resolutions of several hundred meters or higher. One of the most prominent examples is the Weather and Research Forecast model known as WRF-Fire [16], which needs fine terrain and fuel types as input and is applied in various countries around the world [17][18][19].…”
Section: Introductionmentioning
confidence: 99%
“…The second direction taken is to combine highly simplified, empirical-or semi-empirical-based fire models with relatively complete numerical weather prediction models with resolutions of several hundred meters or higher. One of the most prominent examples is the Weather and Research Forecast model known as WRF-Fire [16], which needs fine terrain and fuel types as input and is applied in various countries around the world [17][18][19].…”
Section: Introductionmentioning
confidence: 99%