Flood simulation and forecast capability have been greatly improved, thanks to the advances in data assimilation (DA). Such an approach combines in situ gauge measurements with numerical hydrodynamic models to correct the hydraulic states and reduce the uncertainties in model parameters. However, these methods depend strongly on the availability and quality of observations, thus necessitating other data sources to improve the flood simulation and forecast performances. Using Sentinel-1 images, a flood extent mapping method was carried out by applying a Random Forest algorithm trained on past flood events using manually delineated flood maps. The study area concerns a 50-km reach of the Garonne Marmandaise catchment. Two recent flood events are simulated in analysis and forecast modes, with a +24-h lead time. This study demonstrates the merits of using synthetic aperture radar (SAR)-derived flood extent maps to validate and improve the forecast results based on hydrodynamic numerical models with Telemac2D-ensemble Kalman filter (EnKF). Quantitative 1-D and 2-D metrics were computed to assess water-level time-series and flood extents between the simulations and observations. It was shown that the free run experiment without DA underestimates flooding. On the other hand, the validation of DA results with respect to independent SAR-derived flood extent allows to diagnose a model–observation bias that leads to over-flooding. Once this bias is taken into account, DA provides a sequential correction of area-based friction coefficients and inflow discharge, yielding a better flood extent representation. This study paves the way toward a reliable solution for flood forecasting over poorly gauged catchments, thanks to the available remote sensing datasets.

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 two‐dimensional (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 (EnKF) 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 synthetic aperture radar‐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 recess.

In this study, the hydrological dynamics of the Congo River Basin (CRB) have been analyzed. This is achieved using multiangular and dual‐polarization passive L‐band microwave signal from the Soil Moisture and Ocean Salinity (SMOS) satellite to estimate water surface extent dynamics from 2010 to 2017. The results provide new insights into the poorly characterized wetlands, peatlands, and floodplains dynamics of the CRB. We found that the mean flooded area for the entire CRB is equal to 89,408 ± 20,623 km² corresponding to 2.39% of the entire basin. The results were compared with three land cover maps (European Space Agency‐Climate Change Initiative [ESA‐CCI], International Geosphere‐Biosphere Programme [IGBP], and Global Surface Water Occurrence [GSWO]) and the SWAMPS global dynamic water surfaces product. More inland waters were detected than the four previous products, except along the rivers. Floods and droughts during the last 10 years were also depicted. The knowledge about the CRB hydrological behavior was improved by analyzing the date of maximum floods, the time lag in days between precipitation, water surface extent, and river water height at the outlet of the nine major Congo subbasin. A lag of 67 ± 3 days between rainfall and inundated areas was found in the Upper Congo (r = 0.89). In the Kasai subbasin, no time lag between rainfall and inundation was found (r = 0.86). The contribution of each floodplain to the Congo discharge was also evaluated. In the future, the fusion of the current surface waters with water heights from the SWOT mission can provide further information to the water volumes of the CRB floodplains.

Radar altimetry provides information on the topography of the Earth surface. It is commonly used for the monitoring not only sea surface height but also ice sheets topography and inland water levels. The radar altimetry backscattering coefficient, which depends on surface roughness and water content, can be related to surface properties such as surface soil moisture content. In this study, the influence of surface soil moisture on the radar altimetry echo and backscattering coefficient is analyzed over semi-arid areas. A semi-empirical model of the soil’s complex dielectric permittivity that takes into account that small-scale roughness and large-scale topography was developed to simulate the radar echoes. It was validated using waveforms acquired at Ku and Ka-bands by ENVISAT RA-2 and SARAL AltiKa respectively over several sites in Mali. Correlation coefficients ranging from 0.66 to 0.94 at Ku-band and from 0.27 to 0.96 at Ka-band were found. The increase in surface soil moisture from 0.02 to 0.4 (i.e., the typical range of variations in semi-arid areas) increase the backscattering from 10 to 15 dB between the core of the dry and the maximum of the rainy seasons.

The behavior of the Ka-band backscattering coefficient at nadir and close-to-nadir angles for land applications is poorly documented. The measurements made during a ground-based campaign at Ka-band were performed at nadir and close-to-nadir angles over bare soils for different surface roughness and soil moisture conditions. The resulting backscattering levels exhibited a dynamic range of approximately 23 dB at nadir for soil moisture contents between 5% and 50% m ³ /m ³ over both smooth and rough surfaces. These results were then compared to the geometric optics (GO) and millimeter microwave (MMW) models. Generally, GO finely fits the backscattering coefficients close to nadir, and MMW appeared to fit for larger incidence angles or rough surfaces. The results obtained in this letter can address prelaunch science and engineering considerations for the interferometry-altimetry Surface Water and Ocean Topography mission operating at Ka-band.