Task scheduling method of revisit tasks for satellite constellation towards wildfire management

Global warming increases forest wildfire risks to the economy, environment, and human safety. Continuous satellite monitoring offers accurate wildfire predictions and data-driven decision support. Earth Observation Satellite Constellations(EOSC) enable periodic wildfire tracking through revisit observations. Efficient scheduling of these tasks is crucial for optimal constellation operation in wildfire management. However, the existing EOSC scheduling algorithms rarely concentrates on the scheduling of revisit tasks. In this paper, the revisit task scheduling problem of the EOSC is expressed as a multi-objective model. A time-driven multi-objective optimization method(TDMO) is designed to optimize the constellation scheduling process of wildfire observation tasks. TDMO has a time-driven feature and coupled with revisit time in the task, experiments on different scheduling scenarios show this method is effective in scheduling revisit tasks towards wildfire targets.