## 5 Conclusions  
This research utilizes an MDP algorithm, developed in-house [41], aimed at optimizing imaging mission schedules for SAR satellite constellations. The core of this study lies in addressing the challenges of scheduling in regions with a high density of targets, showcasing the algorithm's effectiveness by contrasting it with the greedy algorithm, which is widely used in existing literature. The formulation of the problem utilizes MILP to ensure the observation of the maximum number of high-profit targets within a set planning horizon. Critical to the mission planning process, parameters such as VTWs, time intervals, and gap times are calculated through satellite orbit propagation using the STK software. The MDP algorithm, by segmenting the mission planning into discrete time intervals based on VTWs, strategically schedules consecutive target imaging. This includes calculating both the observation time duration and gap time to establish a comprehensive schedule for target imaging across various test scenarios. Additionally, this study provides a comparative analysis of optimal sequencing strategies for imaging targets utilizing both the MDP and greedy algorithms. For the numerical simulations, the mission area around the Korean Peninsula is set, with 100 to 1000 targets of various levels of significance and urgency. The satellite constellation, equipped with SAR sensors, is configured as a Walker Delta constellation comprising five inclined orbits, with the satellite count ranging from 8 to 40, over a 7-day mission planning period. The evaluation of 50 test scenarios using both the MDP and greedy algorithms yielded significant findings as follows:  
1. In mission allocation analysis, the quantity of satellites significantly impacts the observation strategy, diminishing the importance of the total number of targets. This highlights the crucial role of satellite count in enhancing observation efficiency and profitability, despite the limited benefits of increasing target numbers. 2. Across all scenarios, the mission success rate for profit surpasses that for target observation, validating the goal of maximizing profit through prioritizing high-value targets--a strategy effectively implemented by the MDP algorithm. 3. Analysis of revisit times reveals that targets of higher value benefit from shorter intervals between observations. Confirmed by box plot analysis, this result highlights the robustness of the MDP algorithm. Lastly, the MDP algorithm, despite taking more time than the greedy algorithm, consistently achieves better outcomes in mission allocation, success rates, and revisit times. With advancements in computing power, the computational efficiency of MDP can be enhanced, making it a more suitable choice for complex satellite mission planning. A notable limitation in our study is the assumption that all satellites function without failure throughout their missions. This overlooks potential operational contingencies, such as technical failures, that could impact mission planning and execution. Addressing this limitation, future research will incorporate the consideration of satellite failures and the necessary re-planning of imaging tasks. This inclusion will provide a more realistic approach to satellite mission planning, acknowledging the complex and unpredictable nature of space operations. Also, we plan to broaden the optimization scope to encompass both imaging missions and communication planning with ground stations. As ground stations become more globally accessible, satellite communication planning emerges as being equally vital as imaging scheduling. This expansion includes integrating tasks such as receiving commands from ground stations, conducting imaging missions, and then transmitting the raw data back to Earth. By optimizing this whole mission workflow, we aim to significantly improve the operational efficiency of satellite constellations. Writing--original draft preparation, K.L. ; writing--review and editing, K.L. and S.L. ; methodology, D.K.