Satellite Remote Sensing Mission Scheduling for Ecological Monitoring: A Learning-Based Multi-region Collaborative Approach

Utilizing remote sensing satellites for ecological area observation to provide timely high-resolution imagery is crucial for detailed ecological analysis. However, the extensive coverage of ecological areas and the limitation that a single satellite can only cover a limited target area at any given time lead to complex scheduling conflicts of imaging resources, thereby reducing observation coverage and mission completion rates. To address these challenges, this paper firstly proposes an multi-satellite cooperative regional target mission scheduling framework that integrates satellites, target areas and ground stations. Specifically, the target areas are segmented into grid spaces, upon which an adaptive scanning area calculation model driven by satellite attitude is developed. Additionally, a strip segmentation method tailored for regional targets is designed to maximize observation efficiency while minimizing resource wastage. The communication constraints between the satellite and the ground station are also fully considered in the framework to ensure that the acquired observation data can be efficiently and reliably transmitted to the ground station. Furthermore, this paper introduces a Multi-Agent Deep Policy Gradient Satellite Regional Mission Scheduling (MSRTS) algorithm that promotes collaboration through a shared resource pool among all agents. Experimental results demonstrate that the proposed MSRTS algorithm significantly enhances the mission completion rate, with an average increase in benefits of approximately 9.72%. These f indings provide robust technical support for future large-scale real-time ecological remote sensing monitoring across multiple regions.

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