INTRODUCING THE GLOBAL MAPPING OF FLOOD DYNAMICS USING GNSS-REFLECTOMETRY AND THE CYGNSS MISSION

This study uses the observations from the Cyclone GNSS (CYGNSS) mission to analyze their potential for a global mapping of the floods dynamics in the pan-tropical area using Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R). We base our analysis on the coherent reflectivity derived from CYGNSS observations. We show that the CYGNSS mission configuration allows a gridding at a spatial resolution of 0.1 degrees (similar to 11 km at the equator), with a time sampling of 1 week. We calculate the average and standard deviation values of reflectivity in the grid pixels at each time step. A Gaussian weighted window of one month is used to fill the gaps which appear in the time series due to the pseudo-random sampling of CYGNSS observations. The maps of these two parameters are then compared to elevation data from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), to Land Cover information from the European Space Agency's (ESA) Climate Change Initiative (CCI), and to a reference set of static inundation maps. We observe a strong correspondence between CYGNSS reflectivity-based parameters, and the percentage of flooded areas established in the literature. The detection of the major floodplains, irrigated crops, open water areas, and the hydrological network using CYGNSS data is clear. We observe some limitations over the areas with high elevation due to the CYGNSS mission specificities - and over the most densely vegetated areas. At some point it could prevent the correct extraction of flood patterns. For a future complete CYGNSS-based flood product, the integration of ancillary data describing the major role of land cover, biomass and topography on the GNSS-R returned signals should be necessary to extract the correct features of water cycle.

Automated summary

This study analyzes the potential of using GNSS Reflectometry (GNSS-R) based on observations from the CYGNSS mission to map flood dynamics in the pan-tropical area. The study shows that CYGNSS observations can effectively detect flooded areas and there is a strong correspondence between the GNSS-R parameters and the percentage of flooded areas. However, there are limitations in high-elevation areas and densely vegetated regions.