Understanding Earth’s surface changes is essential for disaster response, environmental monitoring, and long-term sustainability. High-resolution satellite constellations enable systematic observation of rapidly evolving hazards such as floods, wildfires, earthquakes and deforestation. Within this context, this thesis supports the incubation phase of NASA’s Surface Topography and Vegetation (STV) mission by developing a flexible framework for the analysis of Synthetic Aperture Radar (SAR) satellite constellations for global surface monitoring. Identified as a key priority in the 2017 NASA Earth Science Decadal Survey, the STV mission aims to provide meter-scale topographic measurements with global coverage, independent of weather conditions and daylight. To support these objectives, this work focuses on the identification of repeating ground-track orbital solutions through a perturbation-consistent dynamical formulation, enabling predictable and frequent observations across all latitudes.