ALBERT

Description: Feasibility of sensing mesoscale ocean eddies using spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) measurements is demonstrated for the first time. Measurements of Cyclone GNSS (CYGNSS) satellite missions over the eddies, documented in the Aviso eddy trajectory atlas, are studied. The investigation reports on the evidence of normalized bistatic radar cross section ( σ0 ) responses over the center or the edges of the eddies. A statistical analysis using profiles over eddies in 2017 is carried out. The potential contributing factors leaving the signature in the measurements are discussed. The analysis of GNSS-R observations collocated with ancillary data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis-5 (ERA-5) shows strong inverse correlations of σ0 with the sensible heat flux and surface stress in certain conditions.

Description: Reflectometry of Global Navigation Satellite Systems (GNSS) signals from the ocean surface has provided a new source of observations to study the ocean-atmosphere interaction. We investigate the sensitivity and performance of GNSS-Reflectometry (GNSS-R) data to retrieve sea surface roughness (SSR) as an indicator of sea state. A data set of one-year observations in 2016 is acquired from a coastal GNSS-R experiment in Onsala, Sweden. The experiment exploits two sea-looking antennas with right- and left-hand circular polarizations (RHCP and LHCP). The interference of the direct and reflected signals captured by the antennas is used by a GNSS-R receiver to generate complex interferometric fringes. We process the interferometric observations to estimate the contributions of direct signals and reflections to the total power. The power estimates are inverted to the SSR using the state-of-the-art model. The roughness measurements from the RHCP and LHCP links are evaluated against match-up wind measurements obtained from the nearest meteorological station. The results report on successful roughness retrieval with overall correlations of 0.76 for both links. However, the roughness effect in LHCP observations is more pronounced. The influence of surrounding complex coastlines and the wind direction dependence are discussed. The analysis reveals that the winds blowing from land have minimal impact on the roughness due to limited fetch. A clear improvement of roughness estimates with an overall correlation of 0.82 is observed for combined polarimetric observations from the RHCP and LHCP links. The combined observations can also improve the sensitivity of GNSS-R measurements to the change of sea state.

Description: Sentinel-1 mission with its wide spatial coverage (250 km), short revisit time (6 days), and rapid data dissemination opened new perspectives for large-scale interferometric synthetic aperture radar (InSAR) analysis. However, the spatiotemporal changes in troposphere limits the accuracy of InSAR measurements for operational deformation monitoring at a wide scale. Due to the coarse node spacing of the tropospheric models, like ERA-Interim and other external data like Global Navigation Satellite System (GNSS), the interpolation techniques are not able to well replicate the localized and turbulent tropospheric effects. In this study, we propose a new technique based on machine learning (ML) Gaussian processes (GP) regression approach using the combination of small-baseline interferograms and GNSS derived zenith total delay (ZTD) values to mitigate phase delay caused by troposphere in interferometric observations. By applying the ML technique over 12 Sentinel-1 images acquired between May–October 2016 along a track over Norway, the root mean square error (RMSE) reduces on average by 83% compared to 50% reduction obtained by using ERA-Interim model.