High-Resolution Monitoring of Live Fuel Moisture Content Across Australia

Live Fuel Moisture Content (LFMC) is a key determinant of vegetation flammability and fire behaviour, yet LFMC products have traditionally relied on coarse-resolution sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), limiting their utility for fine-scale fire management. This study introduces the first continental-scale operational LFMC product for Australia derived from Sentinel-2 imagery at 20 m resolution. We developed a Random Forest regression model trained on approximately 680,000 paired Sentinel-2 reflectance and MODIS-LFMC samples (2015–2022) to emulate outputs from the Australian Flammability Monitoring System (AFMS), a MODIS-based pre-operational LFMC product. Model evaluation against AFMS showed strong agreement for grasslands (R2 = 0.83, RMSE = 32.45%) and moderate performance for forests (R2 = 0.43, RMSE = 20.84%) and shrublands (R2 = 0.21, RMSE = 10.28%). Validation using 2279 in situ LFMC measurements from Globe-LFMC 2.0 indicated improved accuracy at homogeneous sites (NDVI CV ≤ 20th percentile: R2 = 0.42, RMSE = 31.39%). Additionally, when validating with a dedicated field campaign specifically designed for Sentinel-2 LFMC assessment, the model achieved its highest accuracy (R2 = 0.53, RMSE = 32.14%), highlighting the importance of tailored ground protocols for satellite product validation. Predicted LFMC also reproduced observed seasonal dynamics at sites with frequent field monitoring. Despite variability across vegetation types, the Sentinel-2 LFMC product effectively captured spatial patterns and seasonal dynamics, providing a step change in monitoring vegetation moisture at landscape scales. This high-resolution dataset offers actionable intelligence for prescribed burning, fuel treatment planning, and fire behaviour modelling in fire-prone environments.