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The ionosphere is a layer of Earth's upper atmosphere that is ionized by solar radiation. It plays a crucial role in the propagation of Global Navigation Satellite System (GNSS) signals, as these signals pass through the ionosphere on their way from the GNSS satellite to the receiver. The irregularities in the ionospheric electron density may have a significant impact on the GNSS signals, causing delays and phase and amplitude scintillations.GNSS reflectometry (GNSS-R) is a promising technique for atmospheric sounding. Multiple studies have been successfully conducted in the recent decade by using GNSS-R ground-based, airborne and spaceborne data e.g., to estimate ionospheric disturbances from the reflected signals. However, further investigations are needed to precisely characterize ionospheric effects for GNSS-R altimetric applications.This study presents simulation results of ionospheric delay for reflection events in tropical regions. The first-order ionospheric effects are estimated along the ray paths by deriving the slant total electron content from the Neustrelitz Electron Density Model (NEDM). The geometry of the simulated events refers to reflectometry records of the SPIRE satellite constellation and the satellite navigation system GPS on 2021/03/01.Initial analysis has shown promising results. As solar activity increases (indicated by solar radio flux F10.7 index), an increase in the total ionospheric phase delay is evident. Between 0h and 8h local time, there is a delay of 2 to 10 meters. For the time interval from 8h to 16h, the delay is from 14 up to 22 meters, with the maximum at noon. In the sunset period from 16h to 24h, the ionospheric delay reduces from 9 to 3 meters, respectively. The height above the Earth’s surface at which the highest amount of electron content is found along the ray path is ~290 km. This altitude corresponds to the F-region which has the highest concentration of free electrons. The analyzed events correspond to elevation angles from 5 to 30 degrees. The highest ionospheric delay is found at elevation angles between 10 and 20 degrees also depending on the local time.

A GNSS data set was recorded for ionosphere sounding on the German research icebreaker Polarstern during the expedition of MOSAiC (Multidisciplinary Observatory for the Study of the Arctic Climate). The GNSS setup comprised a multi-GNSS high-rate (50 Hz) receiver and a multiband GNSS antenna at right-handed polarization. During the one-year expedition (Sep. 2019 to Oct. 2020) the ship drifted with ice floes over the Arctic Ocean for studying the Arctic climate. In addition, the drift gave an excellent opportunity to collect GNSS data for ionosphere sounding over the central Arctic (at lat. > 85° N). More than five months the ship was drifting in the central part of the Arctic where data from permanent GNSS stations are not available. Nearest stations are all located further south. These measurements aboard Polarstern provide ideal conditions to study scintillations induced by disturbances in the Arctic ionosphere (e.g. by polar patches). The coincidence of the expedition period (2019/20) and solar minimum is a small drawback for the study. It limits the probability of strong scintillations in the observations. So, the question arises whether the precision of GNSS phase measurements on a ship is high enough to detect phase scintillations at solar minimum.The standard deviation of detrended phase samples (phase scintillation index) is derived with 30 s resolution for each GNSS link to quantify disturbance. The study focuses in a first approach on GPS data and high elevation angles (> 30°) as these rays propagate rather vertically and stay in the central Arctic with ionospheric piercing points (assumed at 350 km) not far from the ship location (roughly: horizontal distance < 600 km). Three categories of disturbance are identified in this elevation range:Phase index baseline of about 0.1 +/- 0.05 rad, that is the lower limit for this ship-based setup Phase index anomalies from 0.15 to 0.4 rad (and more) that are found in constant directions on the ship (constant relative bearing) and can be attributed to disturbance of ship multipath/shadowing (by mast and chimney) affecting the field-of-view in these directions Phase index anomalies between 0.15 and 0.2 rad, that occur around local noon at the given high latitudes and can be identified as weak scintillations due to ionospheric disturbance in the cusp region (at the convergence of geomagnetic field lines) Compared to a typical ground-based station operating in the Arctic, the ship-based measurements present a rather high baseline index level that is already in the weak scintillation range. The difference between baseline and the cusp-related scintillations is rather small. Parameters (latitude, elevation angle and local time of the observation) are considered to identify the ionospheric scintillations. Furthermore, anomalies induced by ship multipath disturb the scintillation detection and need to be identified and masked out based on relative bearing limits. We conclude that ship-based GNSS is precise enough to detect Arctic phase scintillations even at the minimum of the solar cycle if proper analysis is conducted.

The precision of sea surface altimetry using bistatically reflected signals of the Global Navigation Satellite System (GNSS) is typically one to two orders of magnitude worse than dedicated radar altimeters. However, when the scattering is coherent, the electromagnetic phase of the carrier signal can be tracked, providing precise ranging measurements. Under grazing angle (GA) geometries, the conditions for coherent scattering are maximized, enabling carrier phase-delay altimetric techniques over sea waters. This work presents the first implementation of GA carrier phase sea surface altimetry using data acquired from a spaceborne platform (NASA Cyclone GNSS mission) and transmitted from both GPS and Galileo constellations. The altimetric results show that the measurement system precision is 3/4.1 cm (median/mean) at 20 Hz sampling, cm level at 1 Hz, comparable to dedicated radar altimeters. The combined precision, including systematic errors, is 16/20 cm (median/mean) precision at 50 ms integration (a few cm level at 1 Hz). The wind and wave requirements to enable coherent scattering at GA geometries appear to be below 6 m/s wind and 1.5 m significant wave height, although only 33% of tracks under these conditions present sufficient coherence. Given that this technique could be implemented by firmware updates of existing GNSS radio occultation missions, and given the large number of such missions, the study indicates that the resulting precision and spatio¿temporal resolution would contribute to resolving some submesoscale ocean signals., The work conducted at ICE-CSIC/IEEC was supported by Spanish Grant RTI2018-099008-B-C22 (MCIU/AEI/FEDER, UE). (Corresponding author: Estel Cardellach.)

The spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM) data collected over ocean carry typical feature information about the ocean surface, which may be covered by open water, mixed water/ice, complete ice, etc. A new method based on Doppler spread analysis is proposed to remotely sense sea ice using the spaceborne GNSS-R data collected over the Northern and Southern Hemispheres. In order to extract useful information from DDM, three delay waveforms are defined and utilized. The delay waveform without Doppler shift is defined as central delay waveform (CDW), while the integration of delay waveforms of 20 different Doppler shift values is defined as integrated delay waveform (IDW). The differential waveform between normalized CDW (NCDW) and normalized IDW (NIDW) is defined as differential delay waveform (DDW), which is a new observable used to describe the difference between NCDW and NIDW, which have different Doppler spread characteristics. The difference is mainly caused by the roughness of reflected surface. First, a new data quality control method is proposed based on the standard deviation and root-mean-square error (RMSE) of the first 48 bins of DDW. Then, several different observables derived from NCDW, NIDW, and DDW are applied to distinguish sea ice from water based on their probability density function. Through validating against sea ice edge data from the Ocean and Sea Ice Satellite Application Facility, the trailing edge waveform summation of DDW achieves the best results, and its probabilities of successful detection are 98.22% and 96.65%, respectively, in the Northern and Southern Hemispheres.

Sea level rise and sea state variability due to climate change and global warming are major research topics in the scientific community. Wind speed (WS) and significant wave height (SWH) are usable parameters to monitor the sea state threats and the impact of the ocean weather conditions in coastal areas. GNSS reflectometry (GNSS-R) has shown considerable promise as a remote sensing technique for ocean parameters estimation. Multiple studies have been successfully conducted in the recent two decades by using GNSS-R ground-based, airborne and spaceborne data to retrieve geophysical properties of the sea surface.The focus of this study is to investigate the Doppler shift of the reflected signal as observable to estimate the Doppler spread (DS) and determine its correlation with sea state changes, making use of GNSS-R airborne data in coastal areas. An additional aim is to study the possibility of using the Doppler spread as a metric for coherent GNSS reflectometry for applications such as precise altimetry and precise total electron content (TEC) estimates. An experiment was conducted from the 12th to the 19th of July 2019 along Opal Coast, between the cities of Calais and Boulogne-sur-Mer, France. The experiment consisted of multiple flights at an altitude of ~780m (a.m.s.l). The direct and reflected signals were received by dual-polarized (Right-Handed and Left-Handed Circular Polarizations) antenna mounted on a gyrocopter.A software receiver is used to process the direct and reflected signals from the right-hand channel. The resulting in-phase (I) and quadrature (Q) components (at 50 Hz rate) of the reflected signals are analyzed in the spectral domain every ten seconds to obtain the relative Doppler shift and power estimates. The coherence is established by analyzing the phase observations obtained from I and Q. The sensitivity of the reflected signal estimates and the sea state is determined by the correlation between the Doppler Spread with wind speed and significant wave height. The latter two were obtained from the atmospheric, land and oceanic climate model, ERA5, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).Initial results have shown promising performance at a calm sea (WS: 2.9 m/s and SWH: 0.26 m) and grazing angles. Satellites with low elevations (E < 10°) present a Doppler Spread of 0.3 Hz and its Pearson correlations with respect to WS and SHW are 0.89 and 0.75, respectively. The performance is relatively poor for high elevation events (E > 30°). The DS increases up to 2.1 Hz and the correlation decrease to 0.55 and 0.42 respectively. Coherence conditions are still under study; however, preliminary phase analysis reveals coherent observations at events with elevations below 15° and sea state with a significant wave height of 0.26 m.

Sea ice is a crucial parameter of the Earth’s climate system. Its high albedo compared to water and its insulating effect between ocean and atmosphere influences the oceans’ radiation budget significantly. The importance of monitoring sea-ice properties arises from the high variability of sea ice induced by seasonal change and global warming. GNSS reflectometry can contribute to global monitoring of sea ice with high potential to extend the spatio-temporal coverage of today’s observation techniques. Properties like ice salinity, temperature, thickness and snow cover can affect the signal reflection. The MOSAiC expedition (Multidisciplinary drifting Observatory for the Study of Arctic Climate) gave us the opportunity to conduct reflectometry measurements under different sea-ice conditions in the central Arctic. A dedicated setup was mounted, in close cooperation with the Alfred-Wegener-Institute (AWI), on the German research icebreaker Polarstern that drifted for one year with the Arctic sea ice.We present results from data recorded between autumn 2019 and spring 2020. The ship drifted in this period from the Siberian Sector of the Arctic (October 2019), over the central Arctic (November 2019 until May 2020) towards Fram Strait and Svalbard (reached in June 2020). Profiles of sea-ice reflectivity over elevation angle (range: 1° to 45°) are derived with daily resolution considering reflection data recorded at left-handed (LH) and right-handed (RH) circular polarization. Respective predictions of reflectivity are based on reflection models of bulk sea ice or a sea-ice slab. The latter allows to include the effect of signal penetration down to the underlying water. Results of comparison between LH profiles and bulk model confirm a reflectivity decrease (about 10 dB) when surrounding open water areas is reduced (by freezing) and the ship drifts in compact sea ice.Further results comprise estimates of sea-ice permittivity from mid-elevation range reflectivity (10° to 30°). The median of estimated permittivity 2.4 (period of compact sea ice) lies in the expected range of reported old ice type (mostly second-year ice). The retrieved reflectivity in the low-elevation range (1° to 10°) give strong indication of signal penetration into the dominating second-year ice with influence of sea ice temperature and thickness. We conclude that sea-ice characterization in future can profit form GNSS reflectometry observations. The on-going study is currently extended to the further evolution of Arctic sea ice during winter and spring period of the MOSAiC expedition.

Soil moisture remote sensing on a global scale has been an active area of research over the past few decades due to its essential role in agriculture and in the prediction of some natural disasters. In this regard, GNSS-Reflectometry (GNSS-R) is proven as an efficient tool for the measurement of soil moisture content using remote sensing techniques. GNSS-R is a bi-static radar technique that uses the L-band GNSS signals as sources of opportunity to characterize Earth's surface, due to the fact that the reflected signals are often affected by the properties of the reflecting surface. In the context of this work, it is important to detect and fastly reach the area of interest (reflecting surface) for which the soil moisture content shall be monitored. A GNSS-R setup onboard a gyrocopter meets all the requirements of our application. This paper is dedicated to the study of airborne GNSS-R techniques for soil moisture monitoring using a low-altitude airborne carrier with a high rate (1ms for GPS C/A) carrier-to-noise ratio (C/N0) observations. To cope with the rapid displacement of the satellites footprints along the receiver trajectory, high rate (1000 Hz rate) C/N0 observations are processed. For this purpose, real flight experimentation has taken place on October 19, 2020 for 45 min. During the flight, the gyrocopter maintained a low-altitude of approximately 315m above the ground with an average speed of 95 km/h. Based on that, the size of the major axis of the first Fresnel zones that constitute the detected footprints ranged between 1,316m for a minimum elevation angle of 3° and 15m for a maximum elevation angle of 75°. Concerning the temporal resolution of the application, the raw data were sampled at a frequency of 25MHz and the C/N0 estimates were realized at a rate of 1000Hz.During the flight, an average of 9 GPS satellites have been detected of which 4 GPS satellite signals were extensively analyzed to observe the reflectivity corresponding to land, beach, and sea reflections. After analyzing the Delay Doppler Maps which provides an image of the scattering cross-section in terms of time and frequency and consequently tracking the corresponding signals, the 1ms C/N0 estimations were derived using the in-phase components of the signals as observations. The reflected signals are then linked to the footprints of the satellites and thus to the reflecting surfaces from which each processed signal has reflected using the GPS time, attitude, and position provided by onboard sensors and the GPS time extracted from the digitized GNSS signals. The ultimate aim of this study is to obtain reflectivity measurements from high rate C/N0 observations in order to provide a soil moisture mapping of the studied area, where we notice that the signals reflected from the beach had the best reflectivity followed by sea then land reflections.

Reflectometry measurements have been conducted aboard the German research icebreaker Polarstern during the MOSAiC expedition (Multidisciplinary drifting Observatory for the Study of Arctic Climate). Signals of Global Navigation Satellite Systems (GNSS) were recorded using a dedicated GNSS reflec- tometry receiver for retrieval of sea-ice reflectivity. The primary goal is a reflectometry-based monitoring of sea ice as part of the Arctic climate study. The data set presented here covers the expedition’s first leg (late Sep. to mid Dec. 2019) in the Siberian Sector of the central Arctic (at about 82◦N to 87◦N). Daily profiles of reflectivity are retrieved for satellite elevations < 45◦. In agreement with model prediction the results show best reflectivity contrast (about 5 dB between compact pack-ice and lower ice concentrations) for observations at left-handed circular polarization and elevation angles of 10◦ to 20◦. A daily resolved time series of sea-ice relative permittivity is inverted from the left-handed data. In general, the level of inversion results is at the lower limit of sea-ice values (rel. permittivity of 3 and below), potentially indicating an influence of incoherent volume scattering. Occasional increase of relative permittivity is attributed to the presence of water. Sea-ice profiles show anomalies that are confirmed by enhanced model prediction (slab reflection). A long-term comparison of prediction and retrieved profiles indicates anomalies’ dependence on ice thickness and temperature.

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.

This manuscript reviews recent progress in optical frequency references and optical communication systems and discusses their utilizations in global satellite navigation systems and satellite geodesy. Lasers stabilized with optical cavities or spectroscopy of molecular iodine are analyzed, and a hybrid architecture is proposed to combine both forms of stabilization with the aim of achieving a target frequency stability of 10-15 [s/s] over a wide range of sampling intervals. The synchronization between two optical frequency references in real-time is realized by means of time and frequency transfer on optical carriers. The technologies enabling coherent optical links are reviewed, and the development of an optical communication system for synchronization, ranging and data communication in space is described. An infrastructure exploiting the capabilities of both optical technologies for the realization of a modernized constellation of navigation satellites emitting highly synchronized signals is reviewed. Such infrastructure, named Kepler system, improves satellite navigation in terms intra-system synchronization, orbit determination accuracy, as well as system monitoring and integrity. The potential impact on geodetic key parameters is addressed.

©2019. American Geophysical Union Precipitable water vapor (PWV) is an important parameter for climate research and a crucial factor to achieve high accuracy in satellite geodesy and satellite altimetry. Currently Global Navigation Satellite System (GNSS) PWV retrieval using static Precise Point Positioning is limited to ground stations. We demonstrated the PWV retrieval using kinematic Precise Point Positioning method with shipborne GNSS observations during a 20‐day experiment in 2016 in Fram Strait, the region of the Arctic Ocean between Greenland and Svalbard. The shipborne GNSS PWV shows an agreement of ~1.1 mm with numerical weather model data and radiosonde observations, and a root‐mean‐square of ~1.7 mm compared to Satellite with ARgos and ALtiKa PWV. An improvement of 10% is demonstrated with the multi‐GNSS compared to the Global Positioning System solution. The PWV retrieval was conducted under different sea state from calm water up to gale. Such shipborne GNSS PWV has the promising potential to improve numerical weather forecasts and satellite altimetry.

Climate change has been a major worldwide concern over the last decades. One of its consequences is an acceleration of the coastal erosion in littoral areas such as the Opal Coast, North of France. In this regard, one of the topics of the multidisciplinary, state funded, project MARCO (Recherche marine et littorale en Côte d’Opale) is the highly space and time resolved study of sea state in the English Channel.As part of MARCO, this study has been focused on Global Satellite Navigation Systems Reflectometry (GNSS-R), a bistatic radar technique that uses the signals broadcasted by GNSS satellites as signals of opportunity. A GNSS-R receiver analyzes the signals reaching the receiver directly as well as the signals previously reflected off the Earth surface. Remote sensing application of GNSS-R considers today properties of water bodies, land and ice surfaces. In this work, the objective is to retrieve sea state and related wind speed information from the analysis of direct and reflected GNSS signals.Several sets of GNSS front-end data have been recorded along the Opal Coast, between the cities of Calais and Boulogne-sur-Mer, between the 12th and 19th of July 2019. The signals were sensed by a dual polarization (Right-Handed and Left-Handed Circular Polarizations) antenna mounted on a gyrocopter. Four datasets of ~18min obtained at an altitude of ~780m above sea level at a speed of ~95 km/h are analyzed by studying the RHCP signals received from 9 GPS satellites for each flight. Considering the altitude of the copter, the major axis of the observed first Fresnel zone is of 25m, 70m and 950m for respective satellite elevation angles of 85° (maximum observed), 30° (regular) and 5° (minimum observed). The raw data is sampled at a frequency of 16.368MHz. The in-phase and quadrature components, for both the direct and reflected signals, are obtained at a rate of 50 Hz. The sea state dependent surface reflectivity is estimated every minute.The signals are processed using a software receiver by means of Delay, Phase and Frequency Locked tracking Loops (DLL, PLL, FLL), aided by a modeling of the difference between the direct and reflected paths for the DLL of the reflected signal. The phasors of the resulting in-phase and quadrature components of the reflected signal are analyzed in the spectral domain in order to determine their coherency and subsequently retrieve the sea state. A rough sea yields reflections from a large surface area, resulting in a non-coherent mixture of phasors and a spread peak in the reflected signal spectrum. A calm sea yields specular reflection from small surface area resulting in a spectrum with a sharp peak. Preliminary results show Pearson correlation coefficients between the spectral spread of the peak and ERA5 wind speeds of 0.61 (high elevations) to 0.94 (low elevations).An important contribution of the airborne GNSS-R system applied in this work is the high spatial resolution of the data. The main perspective of this work is to further improve its time resolution, up to 50Hz.

Determination and monitoring of the mean sea level especially in the coastal areas are essential, environmentally, and as a vertical datum. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way which is becoming a reliable alternative for coastal sea-level altimetry. Comparing to traditional tide gauges, GNSS-R can offer different parameters of sea surface, one of which is the sea level. The measurements derived from this technique can cover wider areas of the sea surface in contrast to point-wise observations of a tide gauge. We use long-term ground-based GNSS-R observations to estimate sea level. The dataset includes one-year data from January to December 2016. The data was collected by a coastal GNSS-R experiment at the Onsala space observatory in Sweden. The experiment utilizes three antennas with different polarization designs and orientations. The setup has one up-looking, and two sea-looking antennas at about 3 meters above the sea surface level. The up-looking antenna is Right-Handed Circular Polarization (RHCP). The sea-looking antennas with RHCP and Left-Handed Circular Polarization (LHCP) are used for capturing sea reflected Global Positioning System (GPS) signals. A dedicated reflectometry receiver (GORS type) provides In-phase and Quadrature (I/Q) correlation sums for each antenna based on the captured interferometric signal. The generated time series of I/Q samples from different satellites are analyzed using the Least Squares Harmonic Estimation (LSHE) method. This method is a multivariate analysis tool which can flexibly retrieve the frequencies of a time series regardless of possible gaps or unevenly spaced sampling. The interferometric frequency, which is related to the reflection geometry and sea level, is obtained by LSHE with a temporal resolution of 15 minutes. The sea level is calculated based on this frequency in six modes from the three antennas in GPS L1 and L2 signals.Our investigation shows that the sea-looking antennas perform better compared to the up-looking antenna. The highest accuracy is achieved using the sea-looking LHCP antenna and GPS L1 signal. The annual Root Mean Square Error (RMSE) of 15-min GNSS-R water level time series compared to tide gauge observations is 3.7 (L1) and 5.2 (L2) cm for sea-looking LHCP, 5.8 (L1) and 9.1 (L2) cm for sea-looking RHCP, 6.2 (L1) and 8.5 (L2) cm for up-looking RHCP. It is worth noting that the GPS IIR block satellites show lower accuracy due to the lack of L2C code. Therefore, the L2 observations from this block are eliminated.

Coastal sea level variation as an indicator of climate change is extremely important due to its large socioeconomic and environmental impacts. The ground-based global navigation satellite system (GNSS)-reflectometry (GNSS-R) is becoming a reliable alternative for sea surface altimetry. We investigate the impact of antenna polarization and orientation on GNSS-R altimetric performance at different carrier frequencies. A one-year dataset of ground-based observations at the Onsala Space Observatory using a dedicated reflectometry receiver is used. Interferometric patterns produced by the superposition of direct and reflected signals are analyzed using the least-squares harmonic estimation (LS-HE) method to retrieve sea surface height. The results suggest that the observations from global positioning system (GPS) L1 and L2 frequencies provide similar levels of accuracy. However, the overall performance of the height products from the GPS L1 shows slightly better performance due to more observations. The combination of L1 and L2 observations (L12) improves the accuracy up to 25% and 40% compared to the L1 and L2 heights. The impacts of antenna orientation and polarization are also evaluated. A sea-looking left-handed circular polarization (LHCP) antenna shows the best performance compared to both zenith- and sea-looking right-handed circular polarization (RHCP) antennas. The results are presented using different averaging windows ranging from 15 min to 6 h. Based on a 6-h window, the yearly root mean squared errors (RMSEs) between GNSS-R L12 sea surface heights with collocated tide gauge observations are 2.4, 3.1, and 4.1 cm with the correlation of 0.990, 0.982, and 0.969 for LHCP sea-looking, RHCP sea-looking, and RHCP up-looking antennas, respectively.

Monitoring coastal sea level has gained a large socioeconomic and environmental significance. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) offers various geophysical parameters including sea surface height. We investigate a one-year dataset from January to December 2016 to evaluate the performance of GNSS-R coastal sea levels during different sea states. Our experiment setup uses three types of antenna in terms of polarization and orientation. A zenith-looking antenna tracks Right-Handed Circular Polarization (RHCP) direct signals and two sea-looking antennas capture both Left-Handed Circular Polarization (LHCP) and RHCP reflections. The Singular Spectrum Analysis (SSA) is used for extracting interferometric frequency from the data and calculating the heights. The results indicate that the height estimates from the sea-looking antennas have better accuracy compared to the zenith-looking orientation. The LHCP antenna delivers the best performance. The yearly Root Mean Square Errors (RMSE) of 5-min GNSS-R L1 water levels compared to the nearest tide gauge are 2.8 and 3.9 cm for the sea-looking antennas and 4.7 cm for the zenith-looking antenna with correlations of 97.63, 95.02, 95.35 percent, respectively. Our analysis shows that the roughness can introduce a bias to the measurements.

For the first time, rain effects on the polarimetric observations of the global navigation satellite system reflectometry (GNSS-R) are investigated. The physical feasibility of tracking the modifications in the surface roughness by rain splash and the surface salinity by the accumulation of freshwater is theoretically discussed. An empirical analysis is carried out using measurements of a coastal GNSS-R station with two side-looking antennas in right- and left-handed circular polarizations (RHCP and LHCP). Discernible drops in RHCP and LHCP powers are observed during rain over a calm sea. The power drop becomes larger at higher elevation angles. The average LHCP power drops by ˜ 5 dB at an elevation angle of 45°. The amplitude of the correlation sum shows a dampening, responding to rain rate systematically. The LHCP observations show higher sensitivity to rainfall compared to RHCP observations. The retrieved standard deviation of surface heights shows a steady increase with the rain rate. The derived surface salinity shows a decrease at rains higher than 10 mm/h. This study confirms the potential under environmental conditions of the GNSS-R ground-based station, e.g., with salinity mostly lower than 30 psu, over a calm sea, being a starting point for future investigations. This work was supported by the German Research Centre for Geosciences (GFZ), Potsdam, Germany.

This article is dedicated to the estimation of the GNSS signal carrier-to-noise ratio using the in-phase component of the signals as observations. In a GNSS receiver, it is the statistic of the correlation provided by the code tracking loop that is used to estimate the carrier-to-noise ratio. In fact, carrier-to-noise estimation is used to monitor the performance of GNSS receivers and the quality of the received signals. In this article, we aim at high rate carrier-to-noise estimation, namely the code repetition rate (e.g. 1ms for GPS C/A), in order to maximize the time resolution of carrier-to-noise observations. We show that in a 1-bit quantization receiver, the in-phase component of the signal can provide a direct observation of the signal amplitude, and therefore of the carrier-to-noise ratio. However, the model that links the 1ms rate observations of the in-phase component with the signal amplitude is non-linear. The non-linear expression that links the maximum value of the in-phase correlation component to the signal amplitude is derived. In order to estimate the time varying amplitudes of the signals, we propose an Extended Kalman Filter to reverse the non-linear expression with the noisy observations of correlation provided by the tracking loop. The proposed model and filter inversion method are assessed on synthetic and real data, while investigating the effect of the cross-correlation contribution of the visible satellites on the estimations. We show using real data that, for a 1-bit quantization receiver, the proposed estimator can achieve the same accuracy as a widely known commercial GNSS receiver with a much higher data rate. We also show that the proposed approach can cope with abrupt changes in the observations compared to a classical C / N 0 estimate.

PRETTY is a 3U Cubesat mission by a consortium of Technical University Graz, Seibersdorf Laboratories and RUAG Space GmbH with a planned launch in 2022. The satellite is based on the OPS-SAT platform [1] and will host two payloads, a radiation monitor and a passive reflectometer. Within the present publication we will discuss the current status of the reflectometer payload including the most relevant risks and also their mitigation by prototype developments and measurements. A first operational version of the instrument using commercial off the shelf (COTS) demonstration boards, hosting flight representative components for the RF front end as well as for the digital signal processing has been assembled and integrated with the corresponding hard- and software. While measurement results based on using the output of a global navigation satellite system (GNSS) simulator as input to the PRETTY instrument have been presented earlier, we focus within this publication on the result of field tests on representative hardware in order to evaluate the expected L1 band environment for the mission.

Talk delivered in EGU General Assembly online, 4-8 May 2020, The PRETTY mission is a 3U CubeSat mission, hosting two different payloads, a radiation dosimeter and an interferometric GNSS reflectometer. The intended launch is planned in 2022.

Measurement of soil moisture content on a global scale have gained increased interest over the years, due to its essential role in agriculture and most importantly in predicting the occurrence of natural disasters. This paper is dedicated to a study on GNSS Reflectometry (GNSS-R) using a low-altitude airborne carrier for soil moisture estimation with 1 ms rate of carrier-to-noise ratio observations. The principle of GNSS-R is the exploitation of L-band navigation signals as sources of opportunity to characterize the earth surface, because the reflected signals are often affected by the nature of the reflective surface. To scan large regional surface areas and quickly reach the areas to monitor, a dynamic GNSS-R system is considered.The GNSS-R setup used in this study consists of RHCP and LHCP antennas mounted on the nose of a gyrocopter and of a Syntony front-end GNSS receiver. In addition, the gyrocopter is equipped with a signal digitizer and mass storage devices for digitizing and storing the base-band GNSS direct and reflected signals along the flight. A drone sensors board is also attached to the gyrocopter, which records the gyrocopter’s attitude and position at 1000 Hz rate along its trajectory. To cope with the rapid displacement of the satellites’ footprints along the receiver trajectory, high rate (1 ms) of carrier-to-noise ratio observations are processed with the data collection of the base-band RHCP and LHCP signals.In the context of the study, it is very important to localize the reflective surfaces (satellites’ footprints) from which each processed signal has reflected, and thus detect which areas were scanned during the flight. The link between the reflected signals and the satellites' footprints is based on the GPS time, attitude and position provided by the drone board and the GPS time extracted from the digitized GNSS signals. We show that these parameters allow to determine, at ms rate, the satellites’ footprints locations (i.e. the surface areas) from which each signal has reflected at a specific GPS Time. A Geographic Information System is developed based on this principle to map the measurements obtained from the GNSS-R airborne setup along a real receiver trajectory. The ultimate aim of this study is to link the obtained GNSS-R measurements with the scanned surfaces to provide a soil moisture mapping of the studied area.