In the framework of the Argo program, profiling drifting floats are now routinely launched in the world's oceans. These floats provide (among other information) data about their position, sampled every ten days, representative of their Lagrangian drift. Previous works have shown the interest of assimilating this new type of data. The assimilation of Lagrangian-type data such as position of drifting floats is not straightforward; it involves the careful implementation of a complex, non-linear observation operator but it is of importance for an application in operational oceanography. For instance this work is useful for localized experiments with surface-drifting floats: the information provided by the Lagrangian data could help to improve any operational system aiming to forecast currents for various applications with significant societal impact, such as coastal rescue or pollutant spreading. The sequence of images (pictures from satellite) provides another important source of Lagrangian data: indeed, it describes the displacement of Lagrangian structures (vortex, fronts ...). Finally, this work could provide guidance for the design of the observational network (distribution of large-scale Argo floats / number of localized surface floats / drifting depth, and so on).