Super resolution (SR) techniques are being widely studied and applied on optical images, providing interesting results in the context of image processing. When applied to earth observation (EO) applications, SR is able to provide images with an increased spatial resolution, enabling a quality enhancement of all the postprocessing techniques which are employed over the super-resolved images. This work represents a study performed on Sentinel-2 RGB images focused on enhancing their spatial resolution using a Deep Learning (DL) based approach. DL algorithms have shown great interest in the satellite remote sensing domain both for the high variety of applications that this kind of data has and for the continuous evolution of the satellite sensors.