Early-Phase Performance Analysis of Earth Observation Missions: A Python-based tool for evaluating and optimizing agile constellations

Earth Observation missions are entering a new era characterized by growing demand for near real-time, reliable and high-quality data. In this context, satellites agility, short delivery times, and efficient use of existing ground and relay infrastructure have become key performance drivers. This thesis addresses these challenges by developing and applying a modular simulation framework capable of modeling agile satellite constellations from end to end - from orbital propagation and target access to task scheduling, communication events, and latency computation. At its core is a Python-based tool designed to assess end-to-end latency and compare Earth Observation constellations under realistic operational conditions. The work was carried out at OHB System AG, one of Europe’s leading space prime contractors, within its Pre-development Department, which focuses on early-stage feasibility studies and mission proposals. The tool was therefore conceived as a sizing and analysis framework for system designers to test new ideas rapidly and support initial trade-off studies for future missions. Using this simulation environment, the thesis evaluates the performance of major existing commercial constellations - including COSMO-SkyMed, PAZ, Pléiades, Pléiades Neo, and ICEYE - against a newly proposed European constellation designed under two configurations (medium- and high-revisit), each characterized by different design choices in orbital geometry, payload type and communication architecture. The results highlight how these parameters directly shape coverage, latency and overall mission effectiveness, underscoring also the trade-offs between performance and investment. Beyond the case study, the thesis demonstrates the value of a robust, Python-based framework for early-phase mission analysis, showing how integrated simulation tools can guide the next generation of Earth Observation systems, supporting Europe’s ambition to deploy agile, responsive and secure space infrastructure. These results also lay the ground for future enhancements, such as integration with optimization algorithms to automatically identify the most effective constellation configurations.