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Recent decades have witnessed a tremendous growth in the number of Earth observation satellites (EOSs), which presents a huge challenge for mission planning. For the EOSs with optical sensors particularly, the observation mission is significantly influenced by the uncertainty of cloud coverage, which has been identified as the most dominant factor for the invalidation of remote sensing images. To overcome this uncertainty, uncertainty programming methods, namely, chance constraint programming (CCP), stochastic expectation model, and robust optimization, are put forth. Despite their success, these approaches are limited in that they simplified the complex cloud coverage uncertainty, which may be different from the true cloud conditions, and they did not take the true cloud information into consideration. Motivated by these recent trends toward Big Data of satellite cloud images and machine learning for spatiotemporal prediction, this article explores a dynamic replanning scheme for multiple EOSs based on cloud forecasting. Specifically, we propose a new approach mainly in the following three steps: first, proactive scheduling based on a CCP is implemented and uploaded via ground control; second, cloud forecasting can be continuously conducted relying on the predictive recurrent neural network and the latest satellite cloud image; and third, mission replanning can be conducted according to the initial schedule and relatively accurate cloud information. Simulation results show that the cloud forecasting method is effective, and the replanning approach presents highly efficient and accurate scheduling results.

Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting