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The past few years have seen an accelerating integration of deep learning (DL) techniques into various remote sensing (RS) applications, highlighting their power to adapt and achieving unprecedented advancements. In the present review, we provide an exhaustive exploration of the DL approaches proposed specifically for the spatial downscaling of RS imagery. A key contribution of our work is the presentation of the major architectural components and models, metrics, and data sets available for this task as well as the construction of a compact taxonomy for navigating through the various methods. Furthermore, we analyze the limitations of the current modeling approaches and provide a brief discussion on promising directions for image enhancement, following the paradigm of general computer vision (CV) practitioners and researchers as a source of inspiration and constructive insight.