###### Abstract  
Cloud cover is an important factor limiting the earth observation efficiency of optical imaging satellites. Existing solutions include avoiding cloudy observation time windows by onboard cloud detectors and ground monitors, which are difficult to improve satellite observation efficiency in time. In order to solve the problem, firstly, a Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites cooperation scheme by using cloud cover information provided by GEO meteorological satellite to guide the imaging of LEO optical satellites is proposed, and the operation flow and key elements in this scheme are analyzed. Secondly, Fengyun-4 GEO meteorological satellite and its cloud mask (CLM) products are analyzed. Thirdly, an autonomous mission planning algorithm based on real-time cloud cover information is proposed. Computational results have demonstrated the effectiveness of the proposed GEO-LEO satellites cooperation scheme by taking the actual orbit and payload data of Fengyun-4 and Gaofen-1/2 satellites as examples. cloud cover; optical imaging satellite; meteorological satellite; GEO-LEO satellites cooperation; autonomous mission planning 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 2022142635 202142635 20 also the corresponding observation time window, onboard storage and power are wasted. Furthermore, satellite-ground data downlink resources are occupied when transmitting the such cloud-covered images. Therefore, how to effectively avoid the impact of cloud cover, acquire high-quality image data and improve the efficiency of satellite observation is a key problem that needs to be solved in the practice of optical imaging satellite applications. Many previous studies have concentrated on cloud cover problem in optical imaging satellite applications. The solutions in early studies can be summarized into three categories. Firstly, during the ground mission planning period of imaging, the weather information provided by meteorological departments is considered to generate robust observation schedule based on a priori information on cloud prediction [4]. [PERSON] et al. [5] developed a stochastic integer planning model considering cloud cover based on current meteorological information, and solved by using a Lagrangian relaxation algorithm to obtain the basic scheme, with a rolling adjustment strategy is used to adjust the basic scheme in real-time according to the latest cloud cover conditions. [PERSON] et al. [6] constructed a spatial geometric model of cloud cover, in the model, the cloud cover time window is obtained by spatial analytical calculations, and then a scheduling model considering cloud cover is established. A heuristic algorithm based on maximizing the observation reward is used for the solution. [PERSON] et al. [7] established a cloud cover information description model, and designed a cloud cover time window calculation method based on pre-judge and dichotomy, built an agile satellite (i.e., with steerable imaging sensor) mission planning model considering cloud cover information and solved the model with ant colony algorithm [8]. [PERSON] et al. [9; 10] studied the optical imaging scheduling problem under cloud cover uncertainty. Based on the a priori information of cloud prediction, stochastic expectation value model and branch pricing algorithm, chance constrained planning model and branch cutting plane/column generation heuristic algorithm, robust model, and exact/random sampling heuristic algorithm were designed. However, the methods mentioned above do not consider the real-time changes of cloud state. Secondly, during the in-flight imaging period, the cloud cover information is obtained via the onboard cloud detector and the observation schedule is adjusted dynamically. [PERSON] et al. [2] studied the autonomous decision making problem of Pleiades satellite which has an onboard cloud detector.