Applying optimal transport to data assimilation can be natural in the case where data are images for example. Indeed, optimal transport theory introduces the so-called Wasserstein distance which can be useful to compare images, and it may be more natural than using classical euclidean distances. The interesting points, as well as the methodology, the technical difficulties and some results of applying optimal transport to data assimilation are presented here.