Rapid environmental, socio-economic, and geopolitical changes are accel- erating transformations in land use patterns worldwide. To effectively monitor and predict these dynamics, DTs offer a promising approach by integrating real-time Earth observation data, climate models, AI-driven analytics, and socio-economic indicators. This paper identifies a critical gap in the application of Digital Twins (DT) frameworks for land use change monitoring, which remains underexplored. We propose a novel two-timescale DT architecture designed to track both rapid event-driven land cover changes (such as floods, wildfires, war-induced damage) and gradual long-term transformations, such as climate-induced agricultural shifts and urban expansion. By bridging the gap between advanced Earth observation tech- nologies and decision-making processes, the proposed framework contributes to the development of AI-enhanced DT systems that facilitate climate adaptation, disaster response, and long-term sustainability in dynamic land systems.