While geostationary satellites can provide continuous near-real-time observations, their low spatial resolution makes it difficult to detect small wildfires. Conversely, polar-orbiting satellites are capable of observing small wildfires at high spatial resolution, but can operate only within restricted observation periods. To improve wildfire spread prediction accuracy using polar-orbiting satellite observations, this paper proposes a novel methodology to accurately reproduce wildfire perimeters observed at specific time points by these satellites. The approach employs a wildfire spread simulator combined with a rate of spread (ROS) adjustment factor. The proposed algorithm derives ROS adjustment factors for each fuel model based on differential evolution, achieving up to a 0.4 increase in the Sørensen index when reproducing wildfire perimeter data at given observation times. Incorporating these factors into simulator-based predictions allows comprehensive consideration of external factors affecting wildfire propagation, which have not been sufficiently accounted for in previous methods. Moreover, considering the frequent occurrence of small wildfires in Korea, this study establishes a mapping between major species of trees in Korea and corresponding Fire Behavior Fuel Models (FBFMs). This serves as an example of appropriately matching major species of trees to FBFMs for wildfire spread prediction in countries where FBFMs have not been previously applied. The methodology’s effectiveness is demonstrated using wildfire perimeter data from polar-orbiting satellite observations and ignition points of recent wildfires in Korea. The proposed algorithm is expected to significantly enhance wildfire response by swiftly providing critical information for accurate wildfire spread prediction. This will facilitate prompt and precise countermeasures for small wildfires independent of external conditions such as weather.

Because wildfire spread is strongly influenced by instantaneous gusts, models that use only mean wind speed typically underestimate spread. In contrast, incorporating suppression effects often leads to overestimation. To reduce these errors, this paper newly proposes the concepts of an effective wind speed (EWS) and an EWS coefficient that jointly account for short-range forecast mean wind speed and the maximum gust from numerical weather prediction. The EWS is defined as an EWS coefficient-weighted average of the mean wind speed and maximum gust, so that the simulated perimeter matches the observed wildfire perimeter as closely as possible. Here, EWS refers exclusively to near-surface horizontal wind speed; vertical wind components are not considered. The EWS coefficient is modeled as a function of elapsed time since ignition, thereby implicitly reflecting the level of suppression resource deployment. The proposed frameworks are described in detail using time-stamped perimeters from multiple large-scale wildfires that occurred concurrently in South Korea during a specific period. On this basis, an EWS coefficient suitable for operational use in South Korea is derived. Using the derived EWS for spread prediction, the Sørensen index increased by up to 0.4 compared with predictions based on maximum gust alone. Incorporating the proposed EWS and coefficient into Korean wildfire spread simulators can improve the accuracy and robustness of predictions under extreme weather conditions, supporting safer and more efficient wildfire response.