Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.

Signal-to-noise ratio (SNR) time series acquired by a geodetic antenna were analyzed to retrieve water heights during asymmetric tides on a narrow river using the Interference Pattern Technique (IPT) from Global Navigation Satellite System Reflectometry (GNSS-R). The dynamic SNR method was selected because the elevation rate of the reflecting surface during rising tides is high in the Garonne River with macro tidal conditions. A new process was developed to filter out the noise introduced by the environmental conditions on the reflected signal due to the narrowness of the river compared to the size of the Fresnel areas, the presence of vegetation on the river banks, and the presence of boats causing multiple reflections. This process involved the removal of multipeaks in the Lomb-Scargle Periodogram (LSP) output and an iterative least square estimation (LSE) of the output heights. Evaluation of the results was performed against pressure-derived water heights. The best results were obtained using all GNSS bands (L1, L2, and L5) simultaneously: R = 0.99, ubRMSD = 0.31 m. We showed that the quality of the retrieved heights was consistent, whatever the vertical velocity of the reflecting surface, and was highly dependent on the number of satellites visible. The sampling period of our solution was 1 min with a 5-min moving window, and no tide models or fit were used in the inversion process. This highlights the potential of the dynamic SNR method to detect and monitor extreme events with GNSS-R, including those affecting inland waters such as flash floods.

Global Navigation Satellite System Reflectometry (GNSS-R) is an emerging remote sensing technique for studying land geophysical parameters. The launch of NASA’s Cyclone GNSS (CYGNSS) mission in 2016 provides GNSS-R data in the pan-tropical area with high spatiotemporal resolution. In this study, we analyze the bistatic observations from CYGNSS for a dynamic floods detection. We compute the coherent reflectivity from CYGNSS L1 data and we grid it at a 0.1°, 7 days spatiotemporal resolution. We use a K-means clustering technique to label the CYGNSS pixels based on their time series of reflectivity. Several reflectivity patterns are extracted from the characteristics of each labelled class: low, medium or high values of reflectivity, and constant or variable amplitude throughout the year. Results are compared to static and dynamic inundation maps, elevation from digital elevation models (DEM), and to land cover information to evaluate the potential of CYGNSS observations for mapping flood dynamics at a global scale. Results highlight the influence of the presence of water on the reflected signals recorded by the CYGNSS satellites. First, high reflectivity values are found over permanent water bodies (lakes, large rivers). Then, seasonal floods are identified by a highly variable value of reflectivity throughout the year, with a peak consistent with the maximum extent of inundations. This is clearly identified over some great floodplains in the Orinoco, Amazon and Parana basins, and over irrigated croplands in the Ganges-Brahmaputra, Mekong and Yangtze basins.While the global link between CYGNSS observations and floods is assessed, we have identified some limitations at the regional scale. First, very dense canopy layers in tropical forests reduce drastically the penetration of GNSS L-band signals, as for other microwave remote sensing data. Thus, floodplains in densely vegetated areas are underestimated using CYGNSS dataset only. Secondly, the reflectivity over bare soils as in the Sahara or in Australia is high, creating sometimes a confusion with water bodies. Soil Moisture is also well captured by CYGNSS observations with a similar seasonality and a lower amplitude of reflectivity when compared to flooded regions. Finally, CYGNSS observations are affected by the elevation. Water bodies at high elevation suffer from a reduced amplitude of the signal, but are still detectable. To overcome these limitations, a CYGNSS-based mapping of floods dynamics should integrate additional information from the biomass, the land cover and the elevation. We are currently working on this aspect to supply a 0.1°, 7 days CYGNSS flood product to the hydrological community.