This study investigated the sensitivity of pyrocumulonimbus (PyroCb) induced by the California Creek fire of 2020 to the amount and type of surface fuels, within the WRF-SFIRE modeling system. Satellite data were used to derive fire arrival times to constrain fire progression, and to augment the fuel characterization with better estimates of combustible vegetation accounting for tree mortality. Machine learning was employed to classify standing dead vegetation from aerial imagery, which was then added as a custom fuel class along with the standard Anderson fuel categories. Simulations using this new fuel class produced a larger and more vigorous PyroCb than the control run, however, still under-predicted the cloud top. Additional augmentation of fuel mass to represent the accumulation of dead vegetation on the forest floor further improved the simulations, demonstrating the efficacy of representing both dead standing and fallen vegetation to produce more realistic PyroCb and smoke simulations. Plain Language Summary The fire-generated thunderstorms (pyrocumulonimbus; pyroCb) often associated with mega-fires significantly impact both regional air quality and future fire progression. Accumulation of dry and dead vegetation is a major driver of such phenomena. Therefore, improved knowledge of surface fuel characteristic and impacts on fire and pyroCb behavior is critical to enhance both fire management and smoke forecasts. In this study, we simulated the 2020 Creek fire in CA, a fire strongly influenced by accumulated dead vegetation, using the WRF-SFIRE model. We observed significant improvement in the predicted smoke column extent as a result of implementing the additional fuel category derived from satellite images representing increased fuel load associated with tree mortality.