A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a dynamic range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher and smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

The CYGNSS constellation of eight satellites was successfully launched in December 2016 into a low inclination (tropical) Earth orbit. Each satellite carries a four-channel bistatic radar receiver which measures signals transmitted by Global Positioning System (GPS) satellites and scattered back into space by the Earth surface. Over the ocean, surface roughness, near-surface wind speed and air-sea latent heat flux are estimated from the surface scattering cross section. Over the land, estimates of near-surface soil moisture and imaging of inland water bodies and flood inundation are derived from the surface reflectivity. The measurements are able to penetrate through all levels of precipitation and through most vegetation canopies due to the long radio wavelength at which GPS operates. The number of satellites in the constellation and their continuous data-taking operation produces high spatial sampling density and low temporal revisit times. Over ocean, this makes possible the reliable detection of tropical cyclone intensification and the resolution of diurnal cycles in tropical winds. Over land, diurnal soil moisture variability is resolved and rapidly changing flood inundation events are mapped. Engineering commissioning of the constellation was completed in March 2017 and the mission is currently in its science operations phase. Science data products are regularly produced over ocean for wind speed, surface roughness, and sensible and latent heat fluxes and over land for near surface volumetric soil moisture. Data products currently in development over ocean include tropical cyclone intensity (peak sustained winds) size (radius of maximum winds), extent (34, 50 and 64 knot wind radii), storm center location, and integrated kinetic energy. Over land, data products in development include refined versions of volumetric soil moisture content, flood inundation extent, time-varying inland water body maps, and riverine streamflow rate. An overview and the current status of the CYGNSS mission will be presented, together with updates on terrestrial science data products in development that are related to the terrestrial water cycle.

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 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 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.