Digital twins applications: Linking earth observation, models, and decision-making for multifunctional landscapes

Digital twins (DTs) are virtual replicas of physical systems that stay synchronized with real-world. In agriculture, water, and environmental management, DTs range from hybrid, periodically synchronized bio-physical models used for planning and forecasting, to full, continuously updated virtual systems supporting real-time operational decisions. This document presents empirical evidence of DTs applications to characterize where they are already creating value, why hybrid DTs dominate over full real-time twins, and how we can design fit-for-purpose implementations of DTs). It also summarizes recent evidence from operational deployments and reviews across water resources, agriculture, biodiversity, and climate services, serving as a starting point for exploring how the concept of DTs can be integrated into the science and practice of the Multifunctional Landscapes Science Program (MFL SP). We analyze enabling data infrastructures (e.g., Digital Earth Africa), emerging basin-scale twins (e.g., the Limpopo River Basin), and the combination with high-resolution climate twins (Destination Earth). It also proposes a practical taxonomy tailored to different characteristics, clarifies the distinction between hybrid and full DTs, and provides ready-to-use typologies by function, scale, data intensity, and decision context. We emphasize that hybrid DTs often deliver most of the value at lower cost where data are sparse and decisions are not time-critical, while full DTs add operational value when minutes-to-hours decisions change outcomes (e.g., pressurized water networks, floods, controlled environments). The taxonomy aligns with recent reviews and case studies and is intended for researchers and implementers in LMICs. We integrate findings from sectoral reviews, African community-scale pilots, water-utility cases, and digital foundations policy analyses. We outline priority use cases for MFL—transboundary allocation and drought management, smart irrigation, flood early warning, and biodiversity management—. The paper concludes with a research and learning agenda for development partners and policy makers