基于遥感数据的林火蔓延模拟是根据遥感技术获取可燃物、地形和火点等数据，结合林火蔓延模拟模型预测森林火灾蔓延态势，对森林防火救灾工作具有重要参考意义。本文使用Cell2Fire和FARSITE两种林火蔓延模拟器，对四川凉山和内蒙古那吉林场的2场森林火灾进行模拟，对比了2种模拟器模拟精度。同时，针对连续模拟精度不高的问题，提出一种基于BAI光谱指数与区域生长的过火区重初始化方法，能够自动提取过火区，结合卫星火点数据重初始化林火蔓延模拟器，进而提高连续多天的模拟精度。实验结果表明：（1）FARSITE和Cell2Fire模拟结果较为相似，大多数情况下FARSITE模拟精度略高于Cell2Fire；（2）与连续模拟和VIIRS重初始化模拟方法相比，所提出的过火区重初始化方法能改善长时间模拟误差累积问题和因缺少前期火点导致的模拟不充分问题，使用过火区重初始化方法的Cell2Fire和FARSITE模拟结果精度最高，说明对于提高模拟结果准确性来说，选择合适的模拟方法可能比选择模拟器更重要。

The utilization of remote sensing data for forest fire spread simulation is predicated on the acquisition of critical information such as combustibles, topography, and ignition points through remote sensing technology. This data, when integrated with forest fire spread models, is pivotal for predicting the trajectory of forest fires and serves as a vital reference for forest fire prevention and emergency response operations. This study harnesses two fire spread simulation tools, Cell2Fire and FARSITE, to simulate two forest fire events occurring in the Liangshan of Sichuan and the Naji Forestry Site in Inner Mongolia, comparing the precision of these simulators. Addressing the challenge of suboptimal accuracy in continuous simulations, the paper proposes an innovative reinitialization method for burned area. This method is based on the Burned Area Index (BAI) spectral index combined with region-growing approach. The study compares the simulation precision of FARSITE and Cell2Fire across three methodologies: continuous simulation, VIIRS reinitialization, and the proposed burned area reinitialization. In the Liangshan fire simulations, Cell2Fire projected a faster spread of fire compared to FARSITE, although FARSITE consistently outperformed Cell2Fire in simulation accuracy across all methods. In the simulations for the Naji Forestry Site, FARSITE"s VIIRS reinitialization and burned area reinitialization methods yielded higher precision than Cell2Fire, but its continuous simulation accuracy significantly diminished and underperformed relative to Cell2Fire. When comparing the accuracy of the three simulation methods, the SC values and confusion matrix indicators showed a general agreement, with the higher precision outcomes predominantly emerging from the burned area reinitialization method applied during the mid to later stages of the fires, with most SC values and F1-Scores exceeding 0.8. The conclusions of this article are as follows: 1) The simulation outcomes from FARSITE and Cell2Fire show minor discrepancies, with FARSITE demonstrating superior accuracy in most scenarios compared to Cell2Fire. 2) Utilizing the Burned Area Index (BAI) and regional growth methodologies enables the automatic extraction of burned areas. This approach is not only simple and reliable in extracting results but also saves time and labor costs, making it more suitable for practical applications in forest fire spread simulations. 3) The re-initialization method for burned areas, which integrates automatically extracted burned areas with VIIRS fire spot data for forest fire spread simulations, significantly enhances the mitigation of issues related to insufficient fire spots in the early stages of a fire and the accumulation of errors during prolonged simulations. The simulation outcomes utilizing this method exhibit the highest accuracy within the simulations conducted by Cell2Fire and FARSITE, suggesting that the selection of an appropriate simulation approach may be more crucial for improving the accuracy of simulation results than the choice of a forest fire spread simulator.