Satellite remote sensing is a key technique to understand Ocean dynamics. Due to measurement difficulties, various ill-posed image inverse problems occur, and among them, gridding satellite Ocean altimetry maps is a challenging interpolation of sparse along-tracks data. In this work, we show that it is possible to take advantage of better-resolved physical data to enhance Sea Surface Height (SSH) gridding using only partial data acquired via satellites. For instance, the Sea Surface Temperature (SST) is easier to measure through satellite and has an underlying physical link with altimetry. We train a deep neural network to estimate a time series of SSH using a time series of SST in an unsupervised way. We compare to state-of-the-art methods and report a 13% RMSE decrease compared to the operational altimetry algorithm.