High severity wildfires, driven by a complex interplay between climate change, vegetation growth, and human activity, present a critical threat to human safety and to the ecosystems of California. Forecasting and managing these fire systems is hindered in part by a lack of comprehensive, long-term, and regularly updated data regarding the vegetation fuel loads and forest structural conditions that drive fire behavior. To address this data need, we developed and launched the California Forest Observatory in the fall of 2020, which is a system designed to map forest structure and fuels at fine spatial and temporal scales. An extensive airborne LiDAR training dataset was integrated with multi-sensor satellite data from Sentinel-1, Sentinel-2, and PlanetScope constellations. LiDAR-derived canopy fuel metrics — including canopy height, cover, base height, and ladder fuel density — were modeled using convolutional neural networks, which leveraged spatial context and sensor fusion to generate predictive models at 10-meter and 3-meter resolutions. Canopy bulk density and surface fuel data were estimated from process-based models. The canopy fuel models demonstrated strong performance: 10-meter model predictions ranged from r<sup>2</sup> = 0.51 to r<sup>2</sup> = 0.79, 3-meter performance metrics ranged from r<sup>2</sup> = 0.72 to r<sup>2</sup> = 0.79, with low net bias across all variables, outperforming comparable, recently-released data products. The output data have successfully supported wildfire hazard mapping, utility risk mitigation, and carbon neutrality planning across the state, and are available via PANGAEA (<a href="https://doi.org/10.1594/PANGAEA.989871" target="_blank" rel="noopener">https://doi.org/10.1594/PANGAEA.989871</a>). By providing a data-driven evidence base for monitoring vegetation change, the California Forest Observatory offers a blueprint for the next generation of satellite-based conservation technologies aiming to inform sustainable land management strategies.