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Reflection power derived from GNSS (Global Navigation Satellite Systems) observations and its sensitivity to sea ice concentration are investigated in this paper. A corresponding experiment has been conducted during the Fram Strait cruise of the Norwegian research vessel Lance in summer 2016.The dedicated setup with a GORS (GNSS Occultation Reflectometry Scatterometry) receiver and dualpolarization (left-and right-handed) antenna links recorded 1922 hours of reflection events during the 20-day cruise of the ship. The antenna setup, mounted 25.0 m above the water line, serves to acquire sea surface reflections at grazing angles below 30 • . Within a 5-minute coherent integration period direct and reflected signal contributions can be separated. Except for highest sea states, with roll angle changes of 20 • peak to peak, the separation allows to retrieve the reflection power and quantifies it in cross-, coand cross-to-co polar ratios. The sea ice concentration is inverted from power ratios using a non-linear least-squares algorithm. Additional data of sea ice concentration gathered by a watchman on the ship is used for validation. The inversion results have a 20% resolution in concentration and 3 h resolution in time. The validation shows that the cross-and the cross-to-co-polar data is sensitive to the sea ice concentration. The respective Pearson correlation of 0.75 and 0.67 suggests further studies to foster the application of GNSS data for sea ice reflectometry.

It is well known that reflected signals from Global Navigation Satellite Systems (GNSS) can be used for altimetry applications, such as monitoring of water levels and determining snow height. Due to the interference of these reflected signals and the motion of satellites in space, the signal-to-noise ratio (SNR) measured at the receiver slowly oscillates. The oscillation rate is proportional to the change in the propagation path difference between the direct and reflected signals, which depends on the satellite elevation angle. Assuming a known receiver position, it is possible to compute the distance between the antenna and the surface of reflection from the measured oscillation rate. This technique is usually known as the interference pattern technique (IPT). In this paper, we propose to normalize the measurements in order to derive an alternative model of the SNR variations. From this model, we define a maximum likelihood estimate of the antenna height that reduces the estimation time to a fraction of one period of the SNR variation. We also derive the Cramér–Rao lower bound for the IPT and use it to assess the sensitivity of different parameters to the estimation of the antenna height. Finally, we propose an experimental framework, and we use it to assess our approach with real GPS L1 C/A signals.

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This article is dedicated to the design of a linear-circular regression technique and to its application to ground-based GNSS-Reflectometry (GNSS-R) altimetry. The altimetric estimation is based on the observation of the phase delay between a GNSS signal sensed directly and after a reflection off of the Earth's surface. This delay evolves linearly with the sine of the emitting satellite elevation, with a slope proportional to the height between the reflecting surface and the receiving antenna. However, GNSS-R phase delay observations are angular and affected by a noise assumed to follow the von Mises distribution. In order to estimate the phase delay slope, a linear-circular regression estimator is thus defined in the maximum likelihood sense. The proposed estimator is able to fuse phase observations obtained from several satellite signals. Moreover, unlike the usual unwrapping approach, the proposed estimator allows the sea-surface height to be estimated from datasets with large data gaps. The proposed regression technique and altimeter performances are studied theoretically, with further assessment on both synthetic and real data.