Accurate estimation of live fuel moisture content (LFMC) is essential for reliable wildfire monitoring and fire risk assessment. However, existing LFMC retrieval methods are frequently limited by poor regional transferability, heavy reliance on empirical relationships, and uncertainties stemming from vegetation heterogeneity. These limitations collectively constrain their physical interpretability and operational applicability across broad spatiotemporal scales. To address these limitations, this study proposes a robust, physically based look-up table (LUT) inversion approach utilizing the PROSAIL radiative transfer model. To mitigate ill-posed inversion problems, we optimized the LUT parameterization by integrating site-specific eco-physiological constraints. Specifically, the established biophysical relationship between Leaf Area Index (LAI) and LFMC was employed to constrain the LUT’s parameter space. Subsequent validation against field-measured in situ LFMC data demonstrated a moderate, yet statistically significant correlation between the estimated and observed LFMC values (e.g., r = 0.547 at Glendora Ridge; r = 0.521 at Bitter Canyon). Furthermore, the burned areas observed during the January 2025 Los Angeles wildfire events were predominantly distributed within regions where the estimated LFMC was below the proposed 90% threshold, demonstrating a strong spatial association between critically low vegetation moisture and wildfire occurrence. Overall, this study advances wildfire risk monitoring from traditional meteorology-driven approaches toward a physiology-based framework, providing both theoretical insights and practical tools for fire risk assessment.
