  Flood simulation and forecast capability have been greatly improved thanks to
advances in data assimilation (DA) strategies incorporating various types of
observations; many are derived from spatial Earth Observation. This paper
focuses on the assimilation of 2D flood observations derived from Synthetic
Aperture Radar (SAR) images acquired during a flood event with a dual
state-parameter Ensemble Kalman Filter (EnKF). Binary wet/dry maps are here
expressed in terms of wet surface ratios (WSR) over a number of subdomains of
the floodplain. This ratio is further assimilated jointly with in-situ
water-level observations to improve the flow dynamics within the floodplain.
However, the non-Gaussianity of the observation errors associated with
SAR-derived measurements break a major hypothesis for the application of the
EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of
this paper lies in the treatment of the non-Gaussianity of the SAR-derived WSR
observations with a Gaussian anamorphosis process (GA). This DA strategy was
validated and applied over the Garonne Marmandaise catchment (South-west of
France) represented with the TELEMAC-2D hydrodynamic model, first in a twin
experiment and then for a major flood event that occurred in January-February
2021. It was shown that assimilating SAR-derived WSR observations, in
complement to the in-situ water-level observations significantly improves the
representation of the flood dynamics. Also, the GA transformation brings
further improvement to the DA analysis, while not being a critical component in
the DA strategy. This study heralds a reliable solution for flood forecasting
over poorly gauged catchments thanks to available remote-sensing datasets.
