Abstract: Cloud cover is an important issue for Earth observation satellite imaging.When the proportion of clouds in the remote sensing image is too high，or the specific target is not visible，the remote sensing image will be invalid.Earth observation satellites can choose among multiple observation targets based on cloud forecast information.For the application of Earth observation satellite mission planning，a large-area short-term cloud forecasting method was proposed.First，the cloud motion vector was obtained by the Farneback optical flow method.Subsequently，the predicted cloud image can be obtained by extrapolating the cloud motion vector.Meanwhile，the Laplace operator was introduced to depict the characterization of the diffusion phenomenon in the process of cloud movement.Utilizing the real cloud image sequence data of the FY-2 satellite，the diffusion factor can be optimized through the backpropagation algorithm of the neural network to improve the effect of cloud forecasting.Through the analysis of forecasting results，the introduced Laplace operator method can improve the accuracy of cloud forecasting.The error of the 80% quantile of cloud coverage is about 11.7%.The cloud cover forecasting with this level of accuracy can be used to guide the mission planning of Earth observation satellites.

Key words: cloud forecasting, satellite cloud images, large area, optical flow method, Laplace operator