###### Abstract  
The increasing number of agile earth observation satellites (AEOSs) in orbit have advanced maneuverable capabilities, enabling the AEOS constellation to provide richer observation services. Therefore, observation scheduling in the AEOS constellation is crucial for improving the performance of satellite remote sensing systems. This paper focuses on the problem of distributed observation scheduling in the AEOS constellation, where a period of transition time is required between two consecutive observations, and this constraint depends on the start time of observations. We define a new fitness function that not only maximizes the profit sum but also considers system load balancing. Based on the fundamental idea of a distributed performance impact (PI) algorithm, we develop a PI-based distributed scheduling method (PIDSM) that runs concurrently on all AEOSs via local inter-satellite link (ISL)-based communications. The PIDSM iterates between two phases: _target inclusion_ and _consensus and target removal_. The first phase aims to select the optimal task for each AEOS, while the second phase reaches a consensus over all AEOSs and removes targets that may decrease overall fitness. Experimental results demonstrate that the PIDSM can schedule more targets, reduce communication overhead, and achieve higher fitness values than existing algorithms. Sensitivity analyses further validate the effectiveness of the PIDSM. s 2023 15 1761 10.3390 1761 10.  
the CEOS. In addition, the increasing satellite number in the AEOS constellation brings significant challenges for scheduling. The observation task is associated with a profit that indicates the importance and value obtained by completing this task. The objective of scheduling AEOSs is often to maximize the profit sum of scheduled targets, while satisfying all operational constraints. Among them, the _transition time_ constraint requires that there must be enough period of time for an AEOS to rotate the camera when executing two consecutive observations. Generally, such a transition time is time-dependent, since it is determined by the angular variation which is changed over time. The time-dependent transition constraint increases the complexity for scheduling. To simplify the problem, some researchers [4] relate the transition time only with task sequence instead of angular variation. The onboard resource constraints, such as energy and data storage capabilities, are considered [5; 6]. There are mainly two scheduling approaches for the AEOS observation schedule: centralized and distributed methods. In the former, a centralized server generates a schedule plan for each AEOS. A few studies [7; 8; 9] used the exact algorithm to obtain an optimal solution. For example, by introducing a deterministic mixed integer liner programming (MILP) model, [PERSON] et al. [8] provided the optimality guarantees. [PERSON] et al. [9] obtained the optimal schedule solution using a MILP solver for a constellation of AEOSs. However, due to the complexity of the considered modeling, these exact algorithms are only suitable for solving small instances. To tackle this issue, a number of heuristic and metaheuristic approaches are developed for the AEOS schedule problem [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Specifically, [PERSON] et al. [10] presented the dynamic programming approach for AEOSs in Cubesat constellations based on MILP modeling. [PERSON] et al. [11] used an edge computing framework where a central node and several edge nodes are included. Owing to the simplicity and efficiency, various evolutionary algorithms are applied extensively for scheduling AEOSs [13; 14; 15; 16; 17; 18; 19; 20]. [PERSON] et al. [13] used the travelling salesman problem to model the AEOS scheduling problem and propose a kind of ant colony optimization. [PERSON] et al. [14] aimed to maximize the profit sum but minimize the total task transition time in the emergence of AEOS scheduling.