The Development of Mission Planning Tool for Thailand's Earth Observation Mission

Abstract

Despite the fact that space based remote sensing has been proven as one of the most essential tool for various Earth observation activities, the satellite and its operation cost is tremendously expensive. From this reason, the operation of agile remote sensing satellites has to be performed and planned carefully in order to ensure the efficiency of the mission and utilize those space-borne sensors at their full capacity. In this case, mission planning plays a crucial role since it is the tool that determines the acquisition queues which need to be executed by the satellite. Importantly, these queues have to be deliberately optimized according to numerous conditions and also take into account the dynamic inputs from the users.For remote sensing satellite operation in Thailand, the acquisition orders generally includes long-term, mid-term and urgent order, especially over the area of Thailand and Southeast Asia. In addition, since the satellites are equipped with the optical payload, cloud coverage is a major drawback that reduces directly the success rate of the mission.Moreover, with the consideration of Thailand future remote sensing satellites programs and the constellation plan with alliance satellites, multi-satellite area acquisition has to be taken into account. The objectives of this development is to create the customized mission planning tool that is capable to cover all of these mentioned aspects with satellites utilization optimization in mind. This paper presents the mission planning tool that is currently developing by GISTDA to support Thailand Earth observation activities. The framework of this tool is shown and discussed. In this first development phase, the proposed mission planning methodology is presented in the form of discrete optimization problem which aims to maximize the total gains, as well as minimizing the changes made to the initial plan when re-planning is necessary. To solve the problem, we rely on metaheuristic optimization algorithms. The proposed methodology was implemented and tested with real-world input data. Simulation results of this mission-planning tool is also presented and discussed.