Spatio-Temporal Assessment of a Live Fuel Moisture Content Monitoring Model from an Operational Perspective

Live fuel moisture content (LFMC) is a key determinant of fuel flammability and forest fire danger; however, its operational monitoring remains challenging due to the limited spatial and temporal coverage of field measurements. This study aims to assess the operational suitability of a Random Forest-based methodology for LFMC estimation by extending a previously validated local-scale approach to a regional and multi-year context. Weighted average LFMC was modeled across 67 shrubland plots in the Valencian Region (eastern Spain) from 2017 to 2025 using Sentinel-2 spectral indices and aggregated meteorological variables consistent with prior research. Model performance was evaluated under spatially independent and combined spatio-temporal training–testing scenarios designed to approximate real-world wildfire monitoring conditions. Results show that the model exhibits good spatial transferability when applied to shrubland plots not used during training within the same temporal domain, while temporal extrapolation is more limited and dependent on the stability of climatic conditions represented in the training data, with a marked decline in performance under changing temperature and precipitation regimes. These findings highlight key drivers of LFMC prediction, identify validation strategies under operational constraints, and contribute to the development of scalable monitoring approaches for wildfire danger assessment and fuel management in Mediterranean shrublands.