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Compressive sensing is a new information theory which breaks the traditional Shannon-Nyquist sampling theorem and can perform signal sampling with a small amount of data. Sparse reconstruction is the key factor of compressive sensing from theory to practice. In order to apply compressive sensing effectively to remote sensing imaging, the effect of sparse reconstruction on remote sensing imaging is studied. Based on the sparse reconstruction model, the causes of reconstruction error are analyzed. Meanwhile, according to the typical convex optimization algorithms and greedy algorithms, the reconstruction errors of remote sensing image are evaluated by Peak Signal-to-Noise Ratio (PSNR). In the simulation, the sparse reconstruction performance of remote sensing image is quantitatively investigated with regard to different compression sampling rates and reconstruction algorithms. The result shows that sparse reconstruction algorithm can successfully reconstruct remote sensing image. The algorithms give good reconstruction quality with different compression sampling rates, which can meet the requirements of remote sensing imaging. The conclusion proves the feasibility of applying compressive sensing sparse reconstruction method in remote sensing imaging.