Wildfires in the Eastern Mediterranean cause significant social, economic, and ecological damage. The measured increase in extreme weather events due to climate change – dry spells, multi-year droughts, etc. – has increased the frequency, severity, and intensity of large forest fires in the past two decades. This leads to a positive feedback cycle where underdeveloped communities who rely on natural resources overgraze wildlands and forest understory plants, over-pump groundwater, and over-harvest resources from the forest, leading to even higher water stress and increased potential for wildfire. Existing operational fire-risk index tools produce a very short-termed prediction, estimating fire risk no more than a week in advance. Moreover, standard operational products such as the USGS’s Wind-enhanced Fire Potential Index (WFPI) produce coarse maps with a spatial resolution of 1- km per pixel. These characteristics of current fire risk mapping tools are suited for regional fire hazard mitigation and general awareness but are less useful at the community level. Furthermore, delivering an objective fire-risk map to end-users would not ensure the effective use of this information, because many communities lack the capacity to translate a fire-risk map into an actionable strategy for fire-risk mitigation. This study infuses remote-sensing and socioeconomics innovations. We will develop a high-resolution early fire-risk prediction model, and couple it with a user-oriented translation and communication framework to provide relevant information to end-users. Our remote sensing innovation focuses on physics-driven data-fusion and expansion of an optimal estimation (OE) atmospheric correction algorithm, resulting in 30-m resolution fire-risk maps. Our socioeconomic innovation focuses on the characterization of at-risk communities for vulnerability to wildfire and capacity for action, and based on these, translation of the objective fire-risk maps into actionable mitigation strategies tailored for the end-user.