Live Fuel Moisture Content (LFMC) is one of the most critical variables for understanding fire dynamics, particularly within forest environments. This study intends to develop a daily LFMC indicator to support operational fire danger management services in France. The product is based on in situ observations from the French National Forest Office and is generated using a lightweight expressive neural network model. The network has been designed to generalise well over time and space. It can be integrated directly into land surface models to enable real-time monitoring of vegetation’s hydric status. The modelling framework combines outputs from a physically based land surface model and satellite-derived leaf area index (LAI) observations, providing high-resolution, spatially consistent estimates of land surface over France. To evaluate the model’s generalisation capacity, we implemented complementary cross-validation strategies to test interannual robustness, spatial transferability, and to simulate an operational deployment scenario. Additionally, we performed a robustness analysis to quantify the sensitivity of predictions to training variability. The results demonstrate a strong ability to estimate the range and dynamics of LFMC across most of France. They also identify regions where additional in situ sampling or improved representation could reduce epistemic uncertainty and enhance the reliability of the model.