The reconstruction of high-resolution gridded altimetry maps from irregularly sampled along-track data remains a key chal- lenge in ocean remote sensing science. Operational products use optimal Interpolation (OI) techniques, which may not deal with nonlinear dynamics at short space-time scales. Here, we investigate an analog data assimilation scheme to improve the reconstruction of fine-scale structures. The analog data assim- ilation combines an ensemble Kalman model and a dataset of exemplars issued from high-resolution numerical simulations to perform an exemplar-based spatio-temporal interpolation of along-track data. As a case-study, we consider a region in the South China Sea and demonstrate the proposition ana- log data assimilation outperforms the classical OI by about ' 20% in terms of mean square reconstruction error.