The oceans have a very important role in climate regulation due to its massive heat storage capacity. Thus, for the past decades oceans have been observed by satellites in order to better understand its dynamics. Satellites retrieve several data with various spatial resolution. For instance Sea Surface Height (SSH) is a low-resolution data field where Sea Surface Temperature (SST) can be retrieved in a much higher one. These two physical parameters are linked by a physical relation that can be learned by a Super-Resolution machine learning algorithm. In this work we present a Subpixel Convolutional Deep learning model that takes advantage of the higher resolution SST field to guide the downscaling of the SSH one. The data fields that we use are simulated by a physic based ocean model at a higher sampling rate than the satellites provide. We compared our approach with a convolutional neural network (CNN) model. Our architecture generalized well with validation performances of 3.94 cm RMSE and training performances of 2.65 cm RMSE.