A Review of Spatiotemporal Super-Resolution Mapping for Remote Sensing Data Fusion

Presently, due to the limitations of satellite launch cost and existing technology, it is scarcely possible to obtain single remotely sensed images with both fine-spatial resolution and high temporal resolution at the same time freely. For solving this kind of predicament, an effective method is to fuse multisource remote sensing data by using spatial–temporal super-resolution mapping (STSRM) algorithms. STSRM is developed on the foundation of super-resolution mapping (SRM), which is used for generating land-cover map with a finer spatial resolution by allocating subpixels position in the mixed pixels of coarse remotely sensed images. This review summarizes the existing mainstream models of spatiotemporal SRM and concludes the advantages and limitations of these methods. At the same time, this article analyzes methods of classification accuracy assessment, expounds the existing problems and challenges, and makes a forward-looking prospect for the future development direction of spatiotemporal SRM.