  Relevant comprehension of flood hazards has emerged as a crucial necessity,
especially as the severity and the occurrence of flood events intensify with
climate changes. Flood simulation and forecast capability have been greatly
improved thanks to advances in data assimilation. This approach combines
in-situ gauge measurements with hydrodynamic models, aiming to correct the
hydraulic states and reduce the uncertainties in the model parameters, e.g.,
friction coefficients, inflow discharge. These methods depend strongly on the
availability and quality of observations, thus requiring other data sources to
improve the flood simulation and forecast quality. Sentinel-1 images collected
during a flood event were used to classify an observed scene into dry and wet
areas. The study area concerns the Garonne Marmandaise catchment, and focuses
on recent flood event in January-February 2021. In this paper, seven
experiments are carried out, two in free run modes (FR1 and FR2) and five in
data assimilation modes (DA1 to DA5). A model-observation bias was diagnosed
and corrected over the beginning of the flood event. Quantitative assessments
are carried out involving 1D metrics at Vigicrue observing stations and 2D
metrics with respect to the Sentinel-1 derived flood extent maps. They
demonstrate improvements on flood extent representation thanks to the data
assimilation and bias correction.
