The Cyclone Global Navigation Satellite System (CYGNSS) is a new constellation of eight low earth orbiting spacecrafts that receive both direct and reflected signals from GPS satellites. Coherent reflection of the GPS signal from standing water over land results in a high surface reflectivity (SR) signal in the CYGNSS data. An image processing algorithm is presented which leverages the SR signal to produce a watermask of inland waterbodies at 0.01^o × 0.01^o spatial resolution. The watermask is compared to hand‐drawn maps of inland waterbodies, as well as to the MODIS watermask product. We find that the CYGNSS watermask provides accurate, time‐varying maps that are able to resolve changes in lake and river position and extent. With CYGNSS’ short return time, watermasks can be generated using as little as half a month of data to produce near‐real time maps of flooding events.

This work demonstrates the creation of dynamic inland water body masks at spatial resolutions ranging from 1 to 3 km through the use of a recently developed coherence detector for the delay-Doppler maps produced by the cyclone global navigation satellite system (CYGNSS) constellation. The use of the coherence of the observed measurements reduces many of the uncertainties associated with previous signal-to-noise ratio-based water body detection approaches for CYGNSS. Using data from January 2018 to February 2020 and producing maps representing time intervals ranging from 3 months to 2 years, the water body masks created are found to be associated with a probability of detection that exceeds 80% as compared to the Pekel water mask developed from Landsat observations. The analysis presented in this work highlights the potential of using spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) systems for dynamic inland water body mapping.

Wetlands are the single largest source of methane to the atmosphere and their emissions are expected to respond to a changing climate. Inaccuracy and uncertainty in inundation extent drives differences in modeled wetland emissions and impacts representation of wetland emissions on inter‐annual and seasonal time frames. Existing wetland maps are based on optical or NIR satellite data obscured by clouds and vegetation, often leading to underestimates in wetlands extent, especially in the Tropics. Here, we present new inundation maps based on the Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation, operating in L‐band that is not impacted by clouds or vegetation, providing reliable observations through canopy and cloudy periods. We map the temporal and spatial dynamics of the Pantanal and Sudd wetlands, two of the largest wetlands in the world, using CYGNSS data and a computer vision algorithm. We link these inundation maps to methane fluxes via WetCHARTs, a global wetland methane emissions model ensemble. We contrast CYGNSS‐modeled methane emissions with WetCHARTs standard runs that use monthly rainfall data from ECMWF re‐analysis (ERA5), as well as the commonly used SWAMPS wetland maps. We find that the CYGNSS‐based inundation maps modify the methane emissions in multiple ways. The seasonality of inundation and methane emissions is shifted by two months because of the lag in wetland recharge following peak rainfall. Both inundation and methane emissions also respond non‐linearly to wet‐season precipitation totals, leading to large interannual variability in emissions. Finally, the annual magnitude of emissions is found to be greater than previously estimated.