Interpolation Theory and Artificial Intelligence: A Roadmap for Satellite Data Augmentation

The analysis and processing of satellite remote sensing data have established new paradigms to observe the Earth and to study climate changes. Data collected by sensors and systems operating across the entire frequency spectrum carry on enormous information content. They enable systematic retrieval and characterization of biogeophysical variables. The retrieval of these variables, however, is often hindered by limitations, such as missing data, low spatial and temporal resolutions, and insufficient co-located ancillary data. These challenges arise due to factors, such as sensor limitations, dead pixels, cloud cover, and discontinuous data acquisition. To overcome these issues, numerous techniques have been proposed, ranging from classical interpolation methods and spectral transforms to modern artificial intelligence-based approaches. This article presents a comprehensive and critical review of satellite remote-sensing data augmentation methods, focusing on how interpolation theory and artificial intelligence techniques can be effectively applied to reconstruct missing data and enhance the quality of retrieved biogeophysical variables. This roadmap offers a comprehensive framework that categorizes and evaluates existing methods, highlights their strengths and limitations, and identifies promising areas for future exploration.