## 4 Conclusions  
In this paper, a GEO-LEO satellites cooperation scheme was proposed that uses the cloud cover information provided by GEO meteorological satellites to guide the autonomous mission planning of LEO optical imaging satellites to address the cloud cover problem. At the same time, the intelligence degree of different satellites in the scheme and the ways of coordination between GEO-LEO satellites were analyzed. Under the scheme, the characteristics of the FY-4A satellite and the application of CLM data were analyzed. Furthermore, a mathematical model of onboard autonomous planning was established, and the period of autonomous planning was determined according to the observation characteristics of GEO meteorological satellites. A heuristic algorithm for onboard autonomous planning, which takes into account the timeliness of real-time cloud cover information and the demand for task adjustment was proposed. An experimental scenario with FY-4A and GF-1/2 as satellite resources and 600 ground targets as observation tasks was constructed, and the effectiveness of the proposed GEO-LEO cooperation scheme was verified. The future works of our study are: (1) This study assumes that CLM products from meteorological satellites can be processed onboard. Consequently, onboard real-time cloud detection algorithms which aims to provide fast CLM products needs to be developed; (2) In the GEO-LEO satellites cooperation scheme, we defined a centralized coordination way between satellites. In the future, the highly autonomous satellite system can be performed in a fully distributed coordination, and autonomous mission planning algorithms; and (3) At high latitude area, the GEO-LEO cooperation scheme has application limitation caused by the parallax of the two satellites observations. Such problems are expected to be solved in further detailed studies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cloud Cover** & \begin{tabular}{c} **Number of Tasks** \\ **in Initial** \\ **Schedule** \\ \end{tabular} & \begin{tabular}{c} **Onboard** \\ **Autonomous** \\ **Planning Adjustment** \\ \end{tabular} & \begin{tabular}{c} **Adjust to** \\ **Cloud-Free** \\ **Window** \\ \end{tabular} & \
\begin{tabular}{c} **Fail to Adjust** \\ **to Cloud-Free** \\ **Window** \\ \end{tabular} &
\begin{tabular}{c} **Adjustment** \\ **Ratio** \\ \end{tabular} \\ \hline
5\% & 325 & 296 & 131 & 165 & 91.08\% \\
15\% & 325 & 277 & 121 & 156 & 85.23\% \\
25\% & 325 & 266 & 112 & 154 & 81.85\% \\
35\% & 325 & 260 & 111 & 149 & 80.00\% \\
45\% & 325 & 251 & 106 & 145 & 77.23\% \\
55\% & 325 & 244 & 101 & 143 & 75.08\% \\
65\% & 325 & 229 & 98 & 131 & 70.46\% \\
75\% & 325 & 218 & 94 & 124 & 67.08\% \\
85\% & 325 & 202 & 87 & 115 & 62.15\% \\
95\% & 325 & 172 & 72 & 100 & 52.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Influence of cloud cover thresholds on onboard autonomous planning. **Author Contributions:** Conceptualization, X.C. and Z.L. ; methodology, Z.L., L.Z. and X.C. ; software, Z.L., X.C. and Y.L. ; validation, Z.L., H.C. and X.C. ; formal analysis, Z.L., X.C. and L.Z. ; investigation, F.Z. and D.W.; resources, J.L. ([PERSON]) and J.L. ([PERSON]); data curation, S.L. and L.Z. ; writing--original draft preparation, Z.L. ; writing--review and editing, Z.L. and Y.Z. ; visualization, Z.L. ; supervision, X.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 42171342, 62005320 and 82003901), the National Key Research and Development Program of China (Grant No. 2019 YFE0126600) and the CACMS Innovation Fund (Grant No. CI2021A03902 and CI2021A03901). **Data Availability Statement:** Data used in the reported studies were obtained from websites as indicated in the text. **Conflicts of Interest:** The authors declare no conflict of interest.