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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.

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.

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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.

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.

The problem of coherent Doppler frequency estimation is addressed here applied to the domain of active hydroacoustics in a context of high Signal to Noise ratios. Pulse-to-pulse coherent Doppler sonars allow non-intrusive high-resolution velocity measurements that are often delicate to analyse, due to the nature of the return signal. We present here a novel pulse-to-pulse coherent Doppler estimator using signal processing techniques defined in the circular domain aiming to better reconstruct the noisy phase of the Doppler signal at high temporal resolutions. This Linear Circular estimator was tested on hydroacoustics synthetic measurements and its robustness to noise was demonstrated compared to the standard pulse-pair algorithm. The study suggests its high potential for time-resolved quasi-instantaneous velocity estimation, and turbulence statistics evaluation. The Linear Circular estimator's robustness to noise also suggests potential interests in applying such methods for incoherent scattering evaluation.

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.

Global Satellite Navigation Systems (GNSS) applications like navigation and positioning generally focus on the use of the direct radio signal broadcasted by the navigation satellites. From these signals, very highly precise coordinates can be obtained. However, there is a proportion of signals, that do not reach the receivers directly, that is, the signals that are reflected off Earth’s surface before reaching the receivers. That phenomenon gave way to one of the techniques that is taking an important role in the scope of GNSS remote sensing called GNSS-Reflectometry (GNSS-R). Due to the high reflection coefficient of the water and its importance within the climate system, the ocean is one of the surfaces with greatest interest in GNSS-R research projects. The objective of this study is to retrieve information about ocean height measured through the delay of the signal, and sea state and wind retrieval (ocean surface roughness) from the analysis of the signal amplitude.During this study, GNSS-R measurements were executed along the North Sea coast between the cities of Calais and Boulogne, France, onboard of a gyrocopter. The setup consisted of a front-end data recorder with a right-handed circular polarization (RHCP) antenna. The campaign was conducted in July 2019 within a total of 9h 40m flight time. Each flight was performed at an altitude of about 800 m above sea level going on two legs forth and back along the coast. The legs differed in the distance from the coastline, of 700 m and 2 km, respectively.Reflectometry signal processing involves three data levels. Level (0): The raw data samples of Syntony front-end receiver. Level (1): The Delay-Doppler Map (DDM) of the correlated reflected signal and the carrier phase, from which geophysical information can be derived. And Level (2): height estimation (from signal correlation in delay and frequency domain) and roughness estimation (from signal amplitude).By using the DDM and the carrier phase delay the sea state shall be assessed including the achievable precision and reliability of estimates. An additional aim is also to validate the configuration in terms of the used platform, antenna setup, and flight design.

It has been shown that the earth surface can be observed using Global Navigation Satellite System (GNSS) signals as signals of opportunity. An important advantage of GNSS in this regard is that it provides a global coverage of the earth thanks to dozens of satellites, projected to be 120 by 2030, distributed in various constellations.GNSS signals parameters such as the carrier phase and the amplitude can be used for example for soil moisture estimation, sea ice detection or sea surface altimetry, which is an important indicator for studying climate evolution. As the sea level varies in centimeters, sea surface altimeters have to be very precise. This accuracy can be achieved using satellite altimeters, provided the availability of precise validation and calibration techniques and in-situ experiments. The objective of this study is the definition of an original GNSS buoy system for satellite altimeters calibration.GNSS buoy systems are a cheap and light alternative solution to mareographs and can provide high rate measurements. In our approach using this system, we consider, as observable, the phase difference between incoming GPS-L1 signals at a reference, fixed antenna at ~10m height on the ground and at a buoy antenna on the sea. In an analogy with a GNSS reflectometry system, the buoy can be compared to a specular reflection point, but presents the advantage of collecting the data from all visible satellites at the same location. The signals sensed by both antennas are digitized before processing.Assuming that the horizontal two-dimensions position of the buoy is accurately known by GNSS positioning (which is more efficient in these dimensions than for estimating the height of the buoy), a new phase observable evolving linearly in the [-π , π] interval as a function of the sine of the satellite elevation can be defined. The slope of this linear evolution is proportional to the height between the two antennas, which is the parameter to estimate. For accuracy and robustness purpose, the estimation of this slope is realized using a circular-linear regression technique that includes the fusion of the data from all visible satellites signals. Indeed, we can show that, using the full span of the sines of visible satellites elevations, centimeter accuracy can be reached for integration times as short as a few milliseconds. The GNSS buoy technique described in this work is evaluated on synthetic and real data.

Navigation in constrained areas such as ports or dense urban environments is often exposed to global navigation satellite system (GNSS) satellites masking caused by the infrastructures. In this case, the GNSS positioning is inaccurate or unavailable and proprioceptive sensors are generally used to temporarily localise the vehicle on a map. However, the drift of these sensors rapidly causes the navigation system to fail. In this study, the navigation is computed using magnetometer and GNSS observations defined in an absolute reference frame. The heading measurements are coupled with a digital map of the road network in order to localise the vehicle in a map matching process. The contribution of this study is the proposition of a particle filter that fuses the position and direction observations to estimate the vehicle position. In this context, when the GNSS signal is masked, the observations of direction are used to compute the vehicle position. The proposed filter is defined in both circular and linear domains in order to take into account the nature of the observations. The proposed approach is assessed on real and synthetic data.

Reflection power derived from the global navigation satellite system (GNSS) observations and its sensitivity to sea-ice concentration are investigated in this article. 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 GNSS Occultation Reflectometry Scatterometry (GORS) receiver and dual-polarization (left- and right-handed) antenna links recorded 1922 h of reflection events during the 20-day cruise of the ship. The antenna setup, mounted 25.0 m above the waterline, serves to acquire sea surface reflections at grazing angles below 30°. Within a 5-min coherent integration period, direct and reflected signal contributions can be separated. Except for the 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-, co-, and cross-to-co-polar ratios. The sea-ice concentration is inverted from power ratios using a non-linear least-squares algorithm. Additional data on sea-ice concentration gathered by a watchman on the ship are 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 cross-to-co-polar data are sensitive to the sea-ice concentration. The respective Pearson correlation of 0.75 and 0.67 further suggests studies to foster the application of the GNSS data for sea-ice reflectometry.