Because the landing security of Chang'E-3 is the most critical requirements during the second stage of Chang'E project, the high-resolution landing image is necessary. The super-resolution reconstruction problem for the single Lunar Rover landing image was solved using compressed sensing theory. A super-resolution reconstruction algorithm for sparse representation by using over-complete dictionary was presented. The goal was to reconstruct an original image from its blurred and down-scaled noisy version. The algorithm assumed a local Sparse-Land model on image patches, serving as regularization. The images from Apollo project, CE-1, CE-2 and tests of the second stage of Chang'E project were applied to extract patches for building two dictionaries. The K-SVD algorithm was adopted for training the dictionaries. Through solving optimization problem via Orthogonal Matching Pursuit algorithm, the sparse representation for each low-resolution landing image patch with respect to Al was obtained. The representation coefficients were applied to Ah in order to generate the corresponding high-resolution landing image patch. At the end of the experiment the high-resolution image which satisfied the reconstruction constraint was obtained by using least squares algorithm. Numerical experiments for Lunar Rover landing images from the tests of the second stage of Chang'E project demonstrated the effectiveness of the proposed algorithm. Moreover, the proposed algorithm outperforms bicubic interpolation based method and the algorithm via Yang in terms of visual quality, the Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE).