Abstract: Wildfires are increasing in intensity and severity at an alarming rate. Recent advances in AI and publicly available satellite data enable monitoring critical wildfire risk factors globally, at high resolution and low latency. Wall-to-wall (i.e. spatially continuous and complete) live fuel moisture content (LFMC) maps provide a spatially granular proxy for wildfire risk, and are valuable for both wildfire research and operational response. However, ground-based LFMC samples are both labor intensive and costly to acquire resulting in sparse and infrequent updates. In this work, we explore the use of a pre-trained, highly-multimodal earth-observation based model for large-scale LFMC mapping. Our approach achieves significant improvements over previous methods using randomly initialized models ($>20\%$ reduction in RMSE). We provide an automated pipeline that enables rapid generation of these LFMC maps across the United States, and demonstrate its effectiveness in two regions recently impact by wildfire (Eaton and Palisades).