Analyzing the dynamics of surface water extent and volume is crucial for a better understanding of the global hydrological and biochemical cycles, especially in a context of climate change. However, due to the scarcity of in situ data and their inhomogenous spatial coverage, their use is limited. Remote sensing provides a good opportunity to solve this problem. In this study, we quantify surface water extent (from multispectral imagery) and volume (combining multispectral imagery and radar altimetry) over the Inner Niger Delta (IND) during the 2000-2022 period. The IND, located in Mali, has a wet season (from August to December) where important floods occured. Time series of surface water extent and volume show seasonal variations with maximum values in November and minimal values in April-May. These long time series show important droughts in 2002, 2004, 2011 and 2017, and maximum flood in 2003. In comparison with the digital flooding model, surface water extent from this method show difference of -20%.

Surface water storage is an essential component of the hydrological cycle. Remote sensing offers valuable tools for monitoring both surface water extent from satellite images and water levels from radar altimetry. Combining both information, we were able to estimate the variations of surface water extent and storage in the Lower Mekong Basin from 2000 to 2020. Signatures of the extreme climatic events - floods from 2000 to 2002, of 2011, drought of 2015 clearly appear on both extent and storage. The mean amplitude of these variables shows a strong decrease when comparing the periods of 2000–2010 and 2011–2020. Between these two periods, a large reduction of the annual average number of days with the presence of floods can be observed in most of the Lower Mekong Basin, except around the Tonle Sap (Cambodia) and in some parts of the delta.

This study presents an analysis of radar signature at Ku-band for incidences ranging from 0° to 10° over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, using data from Jason-3 and SWIM. Time-series of radar responses were built over the following environments: stone and sand deserts, Sahelian savannah and floodplain, flooded and non-flooded equatorial forests. Deserts and non-flooded equatorial forest exhibit almost constant responses, decreasing as the incidence angle increases. Similar seasonal variations of the backscattering coefficient between the dry and the wet season are observed at nadir for Jason-3 and SWIM with a decrease in dry season level and amplitude with the increase of the incidence angle. Backscattering at Ku-band can be related to soil roughness, vegetation cover and soil wetness.

Radar altimetry is commonly used to derive water levels over inland water bodies. If lakes and rivers are increasingly covered with radar altimetry virtual stations, wetlands and floodplains are still poorly monitored using this technique. In this study, an unsupervised classification of Ku-band radar altimetry backscattering coefficients from ENVISAT and Jason-2 is performed in the Congo Cuvette Centrale. Comparisons performed against a classification map of the study area shows a good agreement between the water and vegetation classes of the two datasets. Based on these results, radar altimetry-derived water levels are automatically derived over the water classes. Comparisons against radar altimetry-based water stages from Hydroweb database exhibits also a good agreement.

Sea ice concentration, sea ice thickness, and snow depth over sea ice are important physical parameters for modeling the cryosphere. We present here preliminary results of a method to combine passive radiometer, altimeter, and scatterometer, and to evidence their synergy. The benefit of merging the data from several instruments at level 1 is emphasized, through the use of a classification methodology. This work is performed in the framework of the preparation of the future Copernicus missions CIMR and CRISTAL, that will fly at the same time as ASCAT on board MetOp-SG.

Radar altimetry backscattering coefficients can be related to the dynamics of surface properties over land and ice sheets. Temporal variations of the backscattering coefficients from Jason-3 and Sentinel-3A missions were analyzed over land surfaces. Comparisons at crossover points are presented to show the differences in response due to changes in incidence and acquisition modes.

Surface Soil Moisture (SSM) is a key parameter of water and energy balances in semi-arid areas. SSM is linearly related to the radar backscattering coefficients (σ0) over sand. Linear relationships are commonly used for inverting SSM in semi-arid areas from SAR and scatterometer data. Recent studies demonstrated that SSM can also be inversed from radar altimetry backscattering. An inversion method combining radar altimetry σ0and land surface model (LSM) outputs is proposed here.

Radar altimetry, initially developed to observe the surface topography of the ocean, is now commonly used for the monitoring of inland water bodies. In this study, an altimetry-based gauging network was built in the Ogooué River Basin (ORB), in Gabon, Central Africa. It is composed of altimetry-based virtual stations (VS) under the altimetry tracks of ERS-2/ENVISAT/ SARAL, Cryosat-2, Sentinel-3A and Jason-2/Jason-3. River discharge was estimated from 1995 to 2017 combining water level records from several virtual stations close to Lambaréné gauge station and the rating curve from this station. Very good agreement was found in terms of R (>0.95), RMSE and annual discharge.

Altimetry-derived sea surface heights (SSH) from the recently launched Jason-3 and Sentinel-3A missions were validated against records from seven tide gauges located along the French Atlantic coast in the southern Bay of Biscay. Comparisons between Jason-2 and Jason-3 based SSH were performed along-track when they were orbiting 80 seconds apart during the tandem mission that lasted more than six months. The benefits of the Synthetic Aperture Radar (SAR) acquisition mode on Sentinel-3A is compared to the classical Low Resolution Mode (LRM) on Jason-2 and 3 missions are analyzed.