A reasoned bibliography on SAR interferometry applications and outlook on big interferometric data processing

In the past few decades, Synthetic Aperture Radar Interferometry (InSAR) has proven to be a reliable tool for monitoring land surface deformations occurring naturally (landslides, earthquakes, and volcanoes) or due to some anthropogenic activities, such as extraction of underground materials (, e.g., groundwater, oil, and gas) with acceptable accuracy. The availability of SAR data from various satellites have significantly improved this technology further notably with collecting data from different radar frequencies (X-, C-, and L-band), different spatial resolutions, increased revisit times and diverse imaging geometry including both along ascending and descending orbits. This review provides a description about the InSAR state-of-the-art technology and how it has been effectively used for detecting surface deformations. The techniques of Persistent Scatterer Interferometry, Small Baseline Subset, Stanford Method for Persistent Scatterers, and Offset Tracking are discussed. The paper also discusses the strengths and weaknesses of the different InSAR techniques currently employed in detecting surface deformations, concerning the various types of land cover. It then highlights the optimal methodology and data needs for these different land cover types. This work finally dives into the emergence of new technologies for processing big Earth Observation data and discusses the prospects of using machine/deep learning algorithms powered by advanced cloud computing infrastructure to mine new information hidden within InSAR products and associated land-surface deformations.