Cloud cover is a common issue with optical satellite images, severely affecting the quality and the spatiotemporal availability of surface observations. Clouds can critically impact the utility of satellite images by completely covering the ground below, or distorting the measurements collected. While a generally known practice is cloud identification and removal of affected areas, there is also a growing interest in filling the gaps created with the use of powerful data-driven deep learning methods. Images produced by the Sentinel-2 mission come with such cloud occlusions and a common alternative is the use of Synthetic Aperture Radar (SAR) data, as they are nearly independent of the atmospheric conditions and solar illumination. However, SAR data share entirely different characteristics compared to optical data. Even though Sentinel-1 has the ability to provide continuous, day-and-night observations and to overcome various kinds of bad weather conditions or poor air quality (e.g., clouds, rain, fog and smoke), the information captured by this mission is less descriptive and more complex to interpret than that of optical images. In that case, Generative Adversarial Networks (GANs) are used to translate SAR to optical imagery, while many researchers have recently proposed the information fusion of SAR (Sentinel-1) and optical (Sentinel-2) images with different motives.