Scale problem is one of the central issues in land cover mapping from remote sensing imagery

and downscaling land cover data is a difficulty in this domain. Soft classification can provide more information than hard classification at pixel level. However

the spatial location of land cover compositions within each pixel is unknown. To solve this problem

many super-resolution mapping methods have been developed in recent years. In this paper

a review on recent development of super-resolution mapping methods is presented. The review focuses on the research status

major algorithms and accuracy assessment methods of super-resolution mapping. The main drawbacks

research challenges and future directions of super-resolution mapping are discussed.