Rapid  and  reliable  assessment  of  wildfire  spread  is  critical  for  minimizing  ecological  and socioeconomic damage. Polar-orbiting satellites have high spatial resolution but low temporal resolution, limiting their ability to capture the rapid dynamics of wildfire expansion. To address this limitation, we propose  a  near-real-time  framework  for  estimating  wildfire  extent  using  high-frequency  (2-minute) observations from the GEO-KOMPSAT-2A (GK2A) geostationary satellite, employing Visible Infrared Imaging  Radiometer  Suite  (VIIRS)  VNP14IMG  products  as  reference  data.  A  ±10-minute  temporal averaging scheme was introduced to mitigate single-observation noise and enhance detection stability. Model performance was evaluated across six large wildfires in South Korea, with negative samples down-sampled at a ratio of 1:5 relative to positive fire pixels. In repeated random-split (7:3) and region hold-out evaluations, the Extreme Gradient Boosting (XGBoost) model achieved a mean F1 score of 0.958, slightly higher than that obtained by Random Forest (RF; 0.950). For the Uljin (2022) wildfire, XGBoost achieved an F1 score of 0.948, whereas RF achieved a score of 0.741. The superiority of XGBoost was further confirmed via independent full-pixel validation for the Uiseong (2025) and Uljin (2022) wildfires, obtaining precisions of 0.812 and 0.682, respectively, and F1 scores of 0.729 and 0.699, respectively. For both wildfires, RF yielded higher recall but generated a greater number of false positives. These differences may be attributed to the inherent characteristics of the models, with XGBoost’s gradient-boosting approach emphasizing precision and overall accuracy, and RF tending to favor recall, often at the cost of increased false positives. The time- series analysis demonstrated that, with ±10-minute averaging, wildfire growth can be reliably tracked from approximately 10 minutes after ignition onward at 2-minute intervals. This suggests that GK2A observations can be exploited not only for wildfire detection but also for early-stage monitoring of fire spread, thereby supporting rapid decision-making for resource allocation and initial suppression strategies.



Kewords : Wildfire monitoring, Near-real-time, GK2A, VIIRS, Random forest, XGBoost