Live Fuel Moisture Content (LFMC) is a critical determinant of wildfire ignition and spread. Accurate forecasting of LFMC dynamics, particularly at a two-week timescale, is essential for early wildfire danger assessment. While satellite remote sensing provides valuable current and historical observations, it lacks the ability to predict future LFMC dynamics. Meanwhile, although weather forecasts are relatively reliable over short timescales (up to two weeks), LFMC models based solely on meteorological inputs often fall short, particularly when predicting conditions at the species level. To address these limitations, this study introduces a species-specific approach that integrates MODIS-derived LFMC data into the biophysical process-based MEDFATE model to optimize LFMC simulations and enable short-term forecasting based on daily weather projections. A global sensitivity analysis was conducted to identify key input parameters for different tree species within MEDFATE. These parameters guided the development of a cost function that quantifies discrepancies between model-simulated and field-measured LFMC, enabling species-specific model calibration. To enhance model optimization, the global optimal DEoptim algorithm was combined with four-dimensional variational data assimilation (4D-Var) to integrate MODIS-derived LFMC estimates into MEDFATE. Using weather projections, the optimized MEDFATE model produced LFMC forecasts at about a two-week timescale. Time-series measurements of LFMC dynamics for Quercus faginea, Quercus ilex, and Pinus halepensis in Spain, Pinus ponderosa in the USA, and Eucalyptus species in Australia demonstrated that model calibration improved daily LFMC estimates (R2 increased from 0.22 to 0.31; RMSE reduced from 18.71% to 16.04%). Further incorporation of MODIS-derived LFMC data significantly enhanced accuracy (R2 = 0.56; RMSE = 9.75%). Validation across seven wildfire events in Spain, Australia, and the USA confirmed the effectiveness and operational relevance of the approach for early fire warning. These findings underscore the potential of integrating satellite remote sensing and meteorological data into biophysical process-based models to improve tree species-specific LFMC prediction and support proactive fire management.