Using artificial intelligence to monitor live fuel moisture content across France, based on a high resolution land surface analysis

Live Fuel Moisture Content (LFMC) is a key variable for understanding wildfire ignition and propagation, particularly in forest ecosystems. In this study, we develop a daily LFMC product designed to support operational fire danger management services in France. The product is built from in situ measurements provided by the French National Forest Office and estimated using a lightweight yet expressive neural network architecture specifically designed to generalize across space and time. The model can be directly coupled with land surface models, enabling near-real-time monitoring of vegetation hydric stress at national scale.

Our framework integrates outputs from a physically based land surface model with satellite-derived leaf area index observations to produce spatially consistent, high-resolution estimates of land surface variables. Model robustness was assessed through complementary cross-validation strategies to evaluate interannual stability, spatial transferability, and an operational “deployment-like” scenario. In addition, a sensitivity analysis quantified the variability in predictions associated with training randomness and data sampling.

Results show strong accuracy across most regions of France, while revealing specific areas where model uncertainty remains high. These spatially explicit insights highlight where additional in situ sampling or improved process representation could meaningfully reduce epistemic uncertainty. Overall, this work demonstrates the potential of combining AI, process-based modeling and satellite observations to deliver operational LFMC products, ultimately supporting more informed wildfire risk assessment and fire management strategies.