Mapping wetland dynamics in the Congo River basin from GNSS-R and hydrological modeling

Wetlands play a crucial role in hydrological and biogeochemical cycles, and particularly in tropical and sub-tropical regions where they account for up to 3/4 of global methane emissions and act as water storage buffers in the landscape. The Congo being the world's second largest river both in terms of drainage area and annual mean discharge as well as the second largest rainforest area, contains large swaths of wetlands that have nevertheless been poorly studied. Remote sensing arguably offers the only viable strategy for mapping wetlands and their dynamics over the entire Congo River basin. Although there have been efforts that combine different type of sensors (microwave, optical etc.), they have been limited by the fact that most of the inundated areas in the Congo are under dense canopies while the bimodality of the river's hydrograph complicates the identification of the basin's hydrography. Global Navigation Satellite Systems Reflectometry (GNSS-R) is a remote sensing technique that has the potential to overcome some of those limitations. Recent work has shown that such observations from the Cyclone Global Navigation Satellite System (CYGNSS) satellite can successfully enable the mapping of inundation dynamics in wetlands on relatively short time scales. Here, we use CYGNSS satellite observations over the Congo River basin from early 2017 to present to quantify changes in wetland inundated area and the identification of hydrographic features such as floodplain channels in the basin. The mapping results are compared against in-situ hydrographic maps, while the dynamics are reconciled with additional satellite observations of precipitation and soil moisture. Furthermore, we use the derived data to inform and validate an existing hydaulic model of the middle reach of the Congo. Finally, we discuss the implications of GNSS-R observations for mapping wetland dynamics globally especially in the context of new and upcoming missions such as SWOT, NISAR, and HydroGNSS.