Existing satellite video processing methods are mainly based on original video, ignoring the use of invariant background characteristics of staring satellites and easy to be disturbed by rapid light changes. In order to improve the application capability of satellite video, this article establishes the satellite video intrinsic decomposition (SVID) model, including satellite video signal composition model, decomposition constraint with time-spatial unity similarity constraint, static and dynamic components separation by improving tensor robust principal component analysis (TRPCA), and decomposition acceleration based on reflectance transfer. With SVID, intrinsic decomposition and dynamic and static component separation are realized. Five Jilin-1 satellite videos are used to verify the validity, superiority, and potential applications of the proposed algorithm. By comparing with the state-of-the-art intrinsic image decomposition (IID) method and intrinsic video decomposition (VID) method, the experimental results prove the superiority of the SVID method in extracting the reflectance component. In addition, the experimental results also prove that SVID has excellent application ability in scene background analysis and moving target tracking.