Analysis of pan-tropical GNSS-R observations from CYGNSS satellites for floods detection and mapping

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.