GNSS reflection measurements in the form of delay-Doppler maps (DDM) from the CYGNSS constellation can be used to complement soil measurements from the SMAP Mission, which has a revisit rate too slow for some hydrological/meteorological studies. The standard approach, which only considers the peak value of the DDM, is subject to a significant amount of uncertainty due to the fact that the peak value of the DDM is not only affected by soil moisture, but also complex topography, inundation, and overlying vegetation. We hypothesize that information from the entire 2D DDM could help decrease uncertainty under these conditions. The application of deep learning based techniques has the potential to extract additional information from the entire DDM, while simultaneously allowing for incorporation of additional contextual information from external datasets. This work explored the data-driven approach of convolutional neural networks (CNNs) to determine complex relationships between the reflection measurement and surface parameters, providing a mechanism to achieve improved global soil moisture estimates. A CNN was trained on CYGNSS DDMs and contextual ancillary datasets as inputs, with aligned SMAP soil moisture values as the targets. Data was aggregated into training sets, and a CNN was developed to process them. Predictions from the CNN were studied using an unbiased subset of samples, showing strong correlation with the SMAP target values. With this network, a soil moisture product was generated using DDMs from 2018 which is generally comparable to existing global soil moisture products, but shows potential advantages in spatial resolution and coverage over regions where SMAP does not perform well.

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.