The invention discloses a method for removing large-area thick clouds for optical remote sensing images through multi-temporal data. If large-area clouds exist in optical remote sensing images, and non-cloud data exist in other multi-temporal images in the areas, cloud area data can be repaired and reconstructed through complementary information of the data. The method comprises using all temporal non-cloud data for dictionary learning, taking relevance among images into account adaptively, learning an over-complete dictionary and optimal sparse representation coefficients of the images, and repairing and reconstructing the data in thick cloud areas. According to the method for removing large-area thick clouds, the different complementary information in multi-temporal image thick-cloud areas is used, relevance of the images serves as the weight, the image thick-cloud area data are filled by aid of a novel sparse representation theory, and accordingly, not only the high accuracy is obtained, but also the idea for removing large-area thick clouds is expanded, and an important practical significance is provided.