European Space Aqency (ESA)’s PROBA-V Earth observation (EO) satellite enables us to monitor our planet at a large scale to study the interaction between vegetation and climate, and provides guidance for important decisions on our common global future. However, the interval at which high-resolution images are recorded spans over several days, in contrast to the availability of lower-resolution images which is often daily. We collect an extensive dataset of both high- and low-resolution images taken by PROBA-V instruments during monthly periods to investigate Multi Image Super-resolution, a technique to merge several low-resolution images into one image of higher quality. We propose a convolutional neural network (CNN) that is able to cope with changes in illumination, cloud coverage, and landscape features which are introduced by the fact that the different images are taken over successive satellite passages at the same region. Given a bicubic upscaling of low resolution images taken under optimal conditions, we find the Peak Signal to Noise Ratio of the reconstructed image of the network to be higher for a large majority of different scenes. This shows that applied machine learning has the potential to enhance large amounts of previously collected EO data during multiple satellite passes.