Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Wildfire simulations depend on fuel representation. Present models are mainly based the density and properties of different vegetation types. This study aims to improve accuracy WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data estimate load trend index dryness woody vegetation. We updated chaparral timber standard classes in settings. used ESA global above-ground biomass (AGB) SAR load, Landsat normalized difference (NDVI) trends moisture content. These sub-parameters represent dynamic changes spatial variability fuel. simulated two wildfires Israel while three models: original 13 Anderson Fire Behavior model, modified introducing AGB alone, dryness. The model (the basic plus dryness) improved simulation results significantly, i.e., Jaccard similarity coefficient increased 283% average. Our demonstrate potential combining satellite NDVI simulations.