Lakes are considered as sentinels of climate change. Water stored in lake is an important component of the land water cycle. Earth Observations offer a unique tool to monitor lakes extent, water levels variations, and hence changes in water volume combing these two variables or one of them with lakes bathymetry. We present here, surface water volume variations combining lake extent from MODIS and water levels from radar altimetry over 01/1995-04/2024 of Lake Titicaca, the second largest lake in South America. Comparisons between water levels from in-situ gauge and from radar altimetry including the recently launched SWOT mission are presented.

Analyzing the spatio-temporal dynamics of groundwaters is crucial for a better understanding of the global hydrological and biogeochemical cycles, especially with climate change and the intensification of human activities. However, monitoring groundwaters is still complicated due to the lack of in situ data and the weak spatial coverage of these sites. Remote sensing is a good opportunity to address this problem. The method to study water level maps of the groundwater table is based on the use of a dense network of virtual stations produced using radar altimetry and surface water extent obtained by multispectral imagery. The interpolation of these two datasets allows us to produce minimum water level maps associated with the groundwater lebel base at a spatial resolution of 500 m from 2000 to 2022. First results show a link between groundwater anomalies and cumulative rainfall.

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.

The floods are an annual phenomenon on the Pacific coast of Ecuador and can become devastating during El Niño years, especially in the Guayas watershed (32,300 km2), the largest drainage basin on the South American western side of the Andes. In this study, we used ENVISAT ASAR GM SAR images with a spatial resolution of 1 km to map the flooded areas between 2004 and 2008 and study the spatio-temporal dynamics of floods in the Guayas Basin. Maximum of likelihood supervised classification performed on ASAR images acquired during four consecutive dry seasons allowed us to identify five classes of land cover consistent with land use map. From the four wet seasons, we computed standardized anomalies of backscattering coefficient to detect changes between dry and wet season and tested different thresholds to identify flooded areas.