Flooding is one of the most devastating natural hazards to which our society worldwide must adapt, especially as its severity and occurrence tend to increase with climate changes. This research work focuses on the assimilation of 2D flood observations derived from remote-sensing images acquired during overflowing events. To do so, the resulting binary wet/dry maps are expressed in terms of wet surface ratios (WSR) over a number of floodplain subdomains. This ratio is assimilated jointly with in-situ water-level gauge observations to improve the flow dynamics within the floodplain. An Ensemble Kalman Filter with a dual state-parameter analysis approach is implemented on top of a TELEMAC-2D hydrodynamic model. The EnKF control vector is composed of spatially-distributed friction coefficients and a corrective parameter of the inflow discharge. It is extended with the hydraulic states within the floodplain subdomains. This data assimilation strategy was validated and evaluated over a reach of the Garonne river. The observation operator associated with the WSR observations, as well as the dual state-parameter sequential correction, was first validated in the context of Observing System Simulation Experiments. It was then applied to two real flood events that occurred in 2019 and 2021. The merits of assimilating SAR-derived WSR observations, in complement to the in-situ water-level observations, are shown in the parameter and observation spaces with assessment metrics computed over the entire flood events. It is also shown that the hydraulic state correction within the dual state-parameter analysis approach significantly improves the flood dynamics, especially during the flood recession.