Sea surface height and sea state can be inferred from GNSS-R data. However, algorithms for both quantities are distinct and rely on different GNSS-R observables. Sea surface height is inferred from the difference of the measured pseudo-ranges, corrected by a function of the transmitting satellite’s elevation angle. E-GEM will evolve the current state of the art for altimetry algorithms. Regarding sea state, the mean squared slope of the waveforms can be related to the significant wave height or wind speed, under fully developed seas. It can be inferred from the volume (area) of the normalized DDM (or from the area of the waveform) over an area corresponding to the whole glistening zone, from the shape of the tail (trailing edge) of the waveform (also expanding over many lags), or just performing scatterometric measurements around the DDM (or waveform) peak (ratio of reflected signal waveform to direct signal waveform). E-GEM will apply these techniques to the acquired data and evaluate their relative performance experimentally, using trade-off studies to conclude on the optimum algorithms.
GNSS-R over ice shows a quasi-specular reflection with DDMs looking extraordinarily to the ones of the direct signal. This strong coherent return suggests that phase altimetry monitoring may be feasible. In addition, experimental evidence shows that returns from dry-snow layers even a few hundred meter below the surface can be detected by a GNSS-R instrument. E-GEM aims to analyse the data gathered from the UAV-experiment and space-borne experiment to investigate the feasibility of technology and to derive the associated retrieval algorithms.
Over land most of the information lies around the DDM (waveform) peak since the surface roughness is much smaller than in the ocean case. Topography effects may impact seriously the DDM (waveform) re-tracking and therefore the DDM (waveform) shape. For soil moisture applications, E-GEM will thus analyse the scatterometric GNSS-R data over areas with gently topography and generally scarce and dry vegetation to infer the relationship with the surface’s soil moisture. The sensitivity of GNSS-R signal to vegetation biomass will be also studied and exploited in flat areas. Experimental data is so far missing, or at least limited, so the acquired data will help to validate the existing models, from which the observables in a wider range of conditions could be extended.