Dead fuel moisture estimation in mountainous areas of South Korea using satellite data and ground-based observations through a tree-based machine learning approach

Fuel moisture (FM) monitoring is constrained by limited sensor availability, low-resolution data, and inadequate methods, necessitating advanced machine learning (ML) approaches. This study applied tree-based ML models, random forest, extreme gradient boosting (XGB), and light gradient boosting, to estimate dead FM in Gangwon State, South Korea. Input variables included meteorological data, GK-2A AHI radiance, topography, and temporal features, with hyper-parameters optimized via five-fold cross-validation. Results showed high accuracy: RMSE values of 1.20–1.41% and R2 values of 0.94–0.96 for the 10-h FM estimates at 10-min intervals. For daily and hourly mean FM predictions, RMSE values ranged from 0.03 to 0.45% with R2 values of 0.99–1.0. Among methods, XGB yielded the highest performance, while ensemble approaches provided consistent accuracy. For wildfire-risk thresholds (FM < 10%), the models demonstrated high estimation performance, with metrics including an ETS of 0.78 and an accuracy of 0.95. These findings demonstrate the efficacy of ML methods and diverse datasets for FM estimation, offering guidance on model selection based on data availability and computational resources. Accurate FM data can significantly enhance wildfire monitoring, prevention, and related research.