On the Remote Sensing of the Atmosphere and Ocean Using Direct and Reflected GNSS Signals

Abstract

The Global Navigation Satellite Systems (GNSS) signals have shown great potential for remote sensing applications. The ubiquitous GNSS signals in direct or reflected geometry can be processed to retrieve different geophysical parameters of the Earth system’s components. This thesis presents several studies on the performance assessment and enhancement of GNSS-derived remote sensing data products and investigating possible new applications. Exemplary datasets from three classes of GNSS data products are used. The first dataset belongs to GNSS meteorology, which is derived from the processing of direct GNSS signals. The data is among the established GNSS data products with about three decades of data archive. The thesis also presents studies based on reflected signals of Medium Earth Orbiting (MEO) GNSS satellites in bistatic radar configuration. The reflected signals can be received by groundbased receivers or spaceborne receivers onboard Low Earth Orbiting (LEOs) satellites. In this sense, the second part of the thesis focuses on ground-based GNSS-Reflectometry (GNSS-R) measurements with demonstrated applications for environmental monitoring. Finally, a new generation of observations from spaceborne GNSS-R technique is investigated. The first study of this thesis focuses on Precipitable Water Vapor (PWV) time series. Although this data product has been produced for decades, its usage in climate applications depends on its homogeneity verification. This demand stems from the fact that the GNSS-derived PWV time series can have change points due to instrumentation upgrades or new settings in GNSS stations. Therefore, a data homogenization method is developed and tested on real and simulated PWV datasets. The method can identify and correct inhomogeneities in the GNSS tropospheric time series without affecting climate or meteorological signals within the time series. A GNSS-R dataset from a coastal experiment has been used in four studies of this thesis to investigate possible quality improvements of sea surface characterization measurements. The dataset includes polarimetric observations recorded using a dedicated reflectometry receiver with multiple input antennas. The antennas have Right- and Left-Handed Circular Polarizations (RHCP and LHCP) and are installed at zenith and sea-looking orientations. The studies show that polarimetric observations can significantly improve the quality of the GNSS-R measurements. A clear improvement in the sensitivity and performance of GNSS-R sea surface roughness estimates is observed for combined polarimetric observations from the RHCP and LHCP links. The sensitivity is even high-enough to discern the roughness change due to rainfall over a calm sea. The dataset is also used to assess GNSS-R sea-level monitoring under different scenarios. The effects of sea surface roughness, polarization and orientation of the antenna, and the frequency of the GNSS signal are studied. The results show that the roughness can degrade the accuracy of the GNSS-R sea-level measurements. The best GNSS-R altimetric performance is observed when combined multi-frequency measurements are used from a sea-looking antenna with an LHCP design. Finally, two datasets from the new generation spaceborne GNSS-R observations are evaluated for novel applications of the GNSS remote sensing technique. In one of the studies, the feasibility of sensing mesoscale ocean eddies using spaceborne GNSS-R is demonstrated for the first time. A long dataset investigation in this thesis reveals the evidence of changes in GNSS-R Normalized Bistatic Radar Cross-Section (NBRCS) over the ocean eddies. The detected signatures are justified using different properties of the eddies, including the eddy-induced changes in Sea Surface Temperature (SST), the interaction of the eddy surface currents with overpass wind field, and accumulated surfactants brought to the surface by the turbulence associated with the eddies. The last study of the thesis evaluates the spaceborne GNSS-R observations for flood detection and mapping during heavy rainfalls. The study is conducted over an area with a high risk of flooding, requiring constant monitoring with timely observations. The results highlight the potential of the spaceborne GNSS-R for providing the observations with the required sensitivity and short revisit time to detect and map the inundated areas.