Spatiotemporal data fusion is a methodological approach and research domain focused on integrating heterogeneous datasets that possess varying spatial and temporal resolutions to generate data products with simultaneously enhanced spatial and temporal fidelity. This field investigates techniques to effectively combine information from disparate sources, such as remote sensing imagery, to mitigate the inherent limitations of single sensors or data acquisition methods in achieving optimal resolution across both dimensions, thereby enabling more comprehensive analysis and modeling of complex dynamic systems in various scientific and applied domains.