HydroGNSS is a European Space Agency (ESA) 2-satellite mission scheduled for launch in the second half of 2025, equipped to continuously operate dual-frequency, dual-polarization GNSS reflectometry (GNSS-R) receivers. It will also utilize a high-rate complex signal mode, called “coherent channel,” to capture both the in-phase and quadrature components of signals reflected at the specular point during most of the duty cycle. This operation will result in a substantial volume of high-resolution GNSS-R data being available for analysis and applications. The three variables to be provided by the HydroGNSS coherent channel are described here and validated with CYGNSS raw intermediate frequency (IF) signals. The coherence coefficient (CC) and the normalized amplitude (NA), developed from the peak phasors using 4-ms coherent integration time, were compared to the “full entropy” available in the CYGNSS calibrated raw IF product. NA has a similar sensitivity to water surfaces as the full entropy and is not significantly affected by the coherent integration time. CC exhibits a higher sensitivity to small water bodies, yet it strongly depends on the coherent integration time. We also present a new product, the high sampling rate reflectivity (HSRR). It is here defined and compared to CYGNSS${L}1$low rate reflectivity as well as to the high sampling rate (hsr) CYGNSS calibrated raw IF reflectivity, showing a correlation above 0.92. Its effectiveness has been further validated by a physical model, and its uncertainty was preliminarily assessed using data from two nearby tracks. The results demonstrate that HSRR can be a valuable tool for retrieving geophysical parameters, offering a much finer resolution than the 1–2-Hz product.

Monitoring the dynamics and surface conditions of lakes is essential for the understanding of climate change dynamics. Lake water level is one of the variables that needs a particular vigilance, requiring a global and consistent monitoring that can be achieved by spaceborne remote sensing. Satellite radar altimetry has been widely used in the monitoring of inland waters in the past decades, with recent great improvements in the spatial resolution through the new generation of Synthetic Aperture Radar (SAR) altimeters. However, current radar altimetry constellations limits the space-time sampling necessary for a systematic and regular mapping of lakes. GNSS-Reflectometry (GNSS-R) could provide complementary observations to densify the spatio-temporal coverage, allowing hydrologists to access to finer details on the lake surface evolution. In particular, grazing GNSS-R broaden the use of opportunistic GNSS signals due to the smaller elevation angles of the reflecting signals. In this letter we address this issue by comparing the altimetry profiles over Great Lakes of grazing GNSS-R data provided by Spire constellation satellites and SAR altimeter data provided by Sentinel-3 constellation satellites, as a first approach in preparation of inter-techniques hydrology lake water level products. This letter confirms the advantages of fusing observations from both types of remote sensing techniques provided that accurate regional geoid model are available. Furthermore, GNSS-R data could also provide an independent lake water level information to validate the radar altimetry measurements over sites which are not equipped with ground means.

With the evolution of Global Navigation Satellite System (GNSS), more GNSS satellites and civilian signals are available for GNSS Reflectometry (GNSS-R). Developments of new onboard processing strategies can improve the observation performance of spaceborne GNSS-R. To this end, this article proposes a new processing method by coherently combining reflected Global Positioning System (GPS) III L1 C/A and L1C signals. By exploiting the additional signal component, the signal-to-noise ratio of the reflected signal can be significantly improved. Moreover, taking advantage of the narrower auto-correlation function of the combined signal, the spatial resolution and the performance on geophysical applications can be significantly improved. The proposed method has been validated by processing cyclone GNSS (CYGNSS) raw intermediate frequency data including the direct and reflected signals from GPS III satellites. The results indicate that the signal-to-noise ratio (SNR) of the combined reflected waveform can be improved by ~2 dB compared to the L1 C/A waveform. Moreover, the SNR of the combined signal can be improved more efficiently using a longer coherent integration interval compared to the L1 C/A signal. Preliminary altimetric results demonstrate a 35.3%-61.6% improvement in the ranging standard deviation, and a 22.4%-64.4% improvement in the median absolute deviation, compared to L1 C/A measurements. Additionally, the correlation coefficient between combined measurements and wind speed improves by 26.3% on average compared to L1 C/A measurements, and 45.7% for high winds. This article presents a novel GNSS-R onboard signal processing method with improved performance, which can provide a reference for the design of future GNSS-R instruments.

The algorithms to detect the presence of water on land surfaces using the Global Navigation Satellite System (GNSS) reflected signals collected aboard of the future ESA Scout 2-satellite mission HydroGNSS are presented. HydroGNSS will be ready for launch in H2/2024, into polar orbits, and it will operate at dual frequency, dual-polarization, and in two simultaneous acquisition modes (low-rate power and high-rate complex signal modes). The overall strategy to generate level-2, point-by-point along track water classification using all these signals is introduced, yet currently available datasets only permit the validation of the algorithms applied to one single frequency and polarization. Two signal coherence indicators are selected per each acquisition mode, and together with geolocation, large-scale surface roughness, and land cover type are the inputs of the random forest classifier, which is an ensemble learning method, with the monthly Global Surface Water (GSW) as a reference target. The algorithms are tested with NASA/CYGNSS low-rate power delay-Doppler maps (DDMs) and with CYGNSS and U.K./TechDemoSat-1 (TDS-1) raw intermediate frequency (IF) sampled signals. The results of the validation are analyzed at different scales, with results achieving the mission’s 90% accuracy requirement. Final retraining and validation using actual dual-polarization and dual-frequency HydroGNSS data will be conducted once the satellites are in-orbit.

In this study, we propose a method for wind speed retrieval using a random forest (RF) algorithm for Cyclone Global Navigation Satellite System (CYGNSS) data. We first compared CYGNSS data with Soil Moisture Active Passive (SMAP) data and found a certain deviation in the CYGNSS ”young sea, limited fetch” (YSLF) data product for high winds. Then, we used SMAP as the ”ground truth” to train an RF model and applied it to the wind speed retrieval of CYGNSS data. The experimental results show that using the RF algorithm for wind speed retrieval can eliminate noise in the CYGNSS YSLF wind speed data and improve retrieval accuracy. In addition, we explored the impact of different input parameter combinations on model performance and found that using an 11-parameter model in CYGNSS wind speed retrieval can achieve optimal performance. This can provide valuable reference for rapid near-real-time retrieval of tropical cyclones using CYGNSS.

HydroGNSS is the second mission supported by the European Space Agency (ESA) under the Scout program, which is a new framework (3 years from KO to launch, cost ≤ 30 M€) by which ESA aims to demonstrate disruptive sensing techniques or incremental science, while retaining the potential to be subsequently scaled up in larger missions or implemented in future ESA Earth Observation programmes. HydroGNSS consists of a scientific demonstrator that primarily addresses land bio-geophysical variables. The mission is comprised of one satellite (with an option on the second) flying at a low-Earth orbit to collect Global Navigation Satellite System reflections (i.e., Delay Doppler Maps, DDMs) near continuously over the globe. The DDMs are used to generate Level 2 products related to Essential Climate Variables (ECV s), whose estimation defines the primary scientific goal of the mission. In this contribution we outline a review of the ECVs targeted by HydroGNSS, showing some representative results achieved during preliminary studies about the mission. Special emphasis is given to the monitoring of soil freeze-thaw state and soil moisture. ECVs are of great interest and support the sustainable development agenda adopted by the United Nations members in 2015.

This paper presents de development plans of a combined P&L-band reflectometry sensor using signals of opportunity.

As a pilot mission for the Chinese global navigation satellite system reflectometry (GNSS-R) constellation, the BuFeng-1 (BF-1) twin satellites A/B were launched on June 5th, 2019. Using three-month of sample data, this study presents the first evaluation of this mission for soil moisture (SM) estimations. The results show that the surface reflectivity (SR) derived from BF-1 bistatic radar cross section (BRCS) correlates well with the SM active passive (SMAP) SM (mean$R =0.85$), and the resulting BF-1 SM shows good correlation with the SMAP SM [$R =0.94$and root mean square deviation (RMSD) = 0.029] and thein situmeasurements ($R =0.77$and RMSD = 0.049). The evaluation provides supportive information for the production and release of the new version of BF-1 Level 1 data and for the development of new algorithms to retrieve SM using data of this mission.

This article analyzes the contribution of the delay-Doppler map (DDM) observation noise to the uncertainty of global navigation satellite system reflectometry (GNSS-R) ocean scatterometry observables and the retrieved wind speeds. For this purpose, the parameter$K^{p}$, which is commonly used in the traditional microwave scatterometer, is introduced to characterize the relative standard deviation (RSD) of the GNSS-R normalized bistatic radar cross section (NBRCS) measurement. Based on the noise covariance of the DDM measurements, the analytic expressions of RSD are derived for two cases, i.e., the NBRCS computed with one single DDM bin at the specular point and the NBRCS computed from$M\,\,\times \,\,N$DDM bins around the specular point. By analyzing the dependence of the RSD on different system, geometry, and instrument parameters, the simplified models of RSD are derived empirically. As a simple application of the proposed models, the wind speed retrieval errors are computed using parameters from the Cyclone GNSS (CYGNSS) mission. It shows that the wind speed retrieval error due to thermal noise and speckle can be an important error source for the overall wind speed retrieval performance, especially at high wind speed and with the high incidence angle GNSS-R measurements.

Launched on June 5th, 2019, Bufeng-1 twin satellites that carry the Chinese first generation spaceborne GNSS-R instruments started using reflected GNSS signals to perform earth observation. By utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), the preliminary results show that BuFeng-1 has a high agreement compared with ECMWF ERA5 or ASCAT observations in the low-to-moderate wind speed range. In this paper, the measurements of Bufeng-1 will be aligned with the hurricanes observed by Stepped Frequency Microwave Radiometer (SFMR) mounted on NOAA aircraft during 2019 Hurricane Season in a certain spatial-temporal matchup criteria. Then, a power function relationship between Bufeng-1 NBRCS and wind speeds under severe sea states is illustrated in the first time. This study is presented to discuss the limitations and issues for the future research of new version of wind speed product of Bufeng-1 mission.

This article assesses the ocean altimetry performance of spaceborne Global Navigation Satellite Systems reflectometry (GNSS-R) by processing the raw data sets collected by the Cyclone GNSS (CYGNSS) constellation. These raw data sets, i.e., the intermediate frequency signal streams before any receiver processing, are processed on the ground with a software receiver, from which the reflected waveforms of GPS L1, Galileo E1, and BeiDou-3 B1 band open service (OS) signals are generated following the conventional GNSS-R approach. By using different retracking algorithms, the bistatic delays of the reflected signals are derived from these waveforms, in which the retracking biases are removed with the specular point (SP) delay and power information computed from the corresponding waveform model. After applying a set of standard delay corrections, the bistatic delay observations are converted into sea surface height (SSH) measurements and compared with the mean SSH model. Both the random error (precision) and systematic effects (accuracy) are characterized with intratrack and intertrack analyses of the bistatic delay measurements. The two-way ranging precision can reach up to 3.9 and 2.5 m with 1-s GPS and Galileo group delay measurement (a factor of ~2 better for altimetry solution), and its evolution with the signal-to-noise ratio shows good consistency with the theoretical model. A significant delay dispersion of 3.0 m between different tracks is found, which is mainly attributed to the receiver orbit error and ionospheric correction residuals. These results can provide useful inputs for the development of future GNSS-R missions dedicated to ocean altimetry applications.

This work aims to demonstrate the applications of spaceborne Global Navigation Satellite System reflectometry (GNSS-R) over inland waters and wetlands. Raw samples of the GNSS signals reflected from lakes, rivers and wetlands have been recorded occasionally by the TechDemoSat-1 (TDS-1) and Cyclone GNSS (CYGNSS) missions. Strong coherent reflections have been found in the GNSS-R signals from open inland water bodies (lakes and rivers), which would make possible to perform precise surface altimetry measurements using both group delay and carrier phase from reflected signals. In addition, it is also found that GNSS signals reflected off densely vegetated wetlands also present some coherent features from the evolution of their carrier phase, showing the potential of the GNSS-R technique to monitor extremely dense wetlands from spaceborne platforms.

In global navigation satellite systems reflectometry (GNSS-R), the autocorrelation property of the transmitted GNSS signal is an important factor in the delay-Doppler map of the scattered power. The autocorrelation functions (ACFs) of the GPS Level 1 (L1) coarse/acquisition signals exhibit sidelobes. For some of the pseudorandom noise (PRN) codes, the high-amplitude sidelobes are located within one chip from the main lobe and, thus, broaden or narrow its width. These PRN-dependent ACF deviations can introduce significant biases into the GNSS-R scatterometric observables and the wind speed measurements. Simulation results for Cyclone GNSS (CYGNSS) mission show that the ACF deviation induced wind speed error can be larger than 10% (e.g., 2-3 m/s for 20 m/s and 7-8 m/s for 40 m/s wind speeds). These effects can be corrected efficiently by applying empirical factors on the CYGNSS L1 observables.

This work is to demonstrate spaceborne altimetry over sea ice using coherent component of reflected Global Navigation Satellite System (GNSS) signal. The raw samples of sea ice reflected GNSS signal collected by the UK TechDemoSat-l (TDS-l) mission are processed. Strong coherent components are found in sea ice reflected GNSS signal received in space at different elevation angles, which would make it possible to perform precise surface altimetry measurements using the carrier phase information of the reflected signals. In addition, the coherent reflected signals can be integrated coherently for a longer period to increase the signal to noise ratio, which would make it possible to demonstrate the interferometric GNSS-R (iGNSS-R) altimetry technique in space without very high gain antenna. In this presentation, the altimetric results of coherent GNSS reflections with both carrier phase and code phase will be discussed.

We present a new passive bi-static radar measurement concept that employs digital satellite TV signals transmitted from geostationary orbit as signals of opportunity to measure precipitation. We also present experimental results using artificial targets, and provide results on measured Doppler frequencies as well.

This paper analyzes the ocean altimetry precision of global navigation satellite systems' reflectometry (GNSS-R) by characterizing the noise statistics of the measured waveform. For this purpose, we first investigate the dependence of the altimetry precision on the statistics of the observed waveform and then derive an analytical model of the waveform statistics after incoherent averaging. The main data set used for altimetry analysis and model validation is the GPS L5 signals obtained from an airborne experiment. Two different delay estimators, based on the leading-edge derivative (DER) and the waveform fitting (FIT) over the leading edge, have been implemented. Later, the relationship between the statistics of the observed waveform and the altimetry precision is derived and validated for both the estimators with the airborne data. Then, the analytical model of the statistics of the incoherently averaged waveform is built from the correlation properties of the complex waveform, and also validated with both airborne and the TechDemoSat-1 spaceborne data. After that, the proposed waveform statistics and altimetry precision models are applied to spaceborne cases with different orbit altitudes. The altimetry performance of different onboard processing methods, i.e., conventional GNSS-R and interferometric GNSS-R, and delay estimators, i.e., DER and FIT, are preliminarily assessed. Finally, the dependence of altimetry precision on different parameters is analyzed and a simplified model of altimetry precision is proposed for the spaceborne case.

Characterizing, understanding, and projecting changes in atmospheric methane and terrestrial water storage require information about wetland dynamics, which remains a major gap in existing knowledge. Despite recent advances in satellite monitoring techniques, dynamic wetland mapping needs to be improved for more accurate global inventories and also to monitor variabilities at sub-km scale over multiple decades. It has been shown that Global Navigation Satellite Systems (GNSS) Reflectometry (GNSS-R) signatures off inundated wetlands can be identified under different vegetation conditions including a dense rice canopy and a thick forest with tall trees, where optical sensors and monostatic radars provide limited capabilities. In this study, we will further investigate the capabilities of the GNSS-R technique for wetland monitoring from aircraft platforms.

Due to the launch of a number of dedicated GNSS-R satellite missions during the last years, there is a potential raise of research interest in this field. This paper presents an analysis and simulation tool for the GNSS-R community: wavpy. More than just a simple waveform simulator, this library provides a set of object-oriented classes dedicated to each of the different elements that characterize a GNSS-R scenario. Then, the user can focus on just a particular piece of analysis or, by exploiting their combined synergies, to perform a more comprehensive simulation exercise.

This work is to analyse the ocean altimetry precision of Global Navigation Satellite Systems Reflectometry (GNSS-R) based on the statistics of observed waveform. The GPS L5 signals obtained from an airborne experiment are used for this study. The altimetry precision is analysed for two specific algorithms, based on the leading edge derivative (DER) and the leading edge fitting (FIT), in which the relationship between the altimetry precision and the waveform statistics is established and validated. Then, we introduce an analytical model of the statistics of the incoherently averaged waveform, which has been validated with both airborne and spaceborne data. Finally, as an example, the altimetry precision is predicted for the GEROS-ISS mission, and preliminary results are presented.

Since the nadir 1-second altimetry precision was first estimated to be of 56 cm when the PARIS concept was developed back in 1993 (it was assumed the interferometric processing of GPS P-code signals and a 4×4 m×m receiving antenna at 700 km orbital altitude) [1], many detailed analyses and experiments have been conducted leading to far more optimistic values, as low as some 15 cm (using the interferometric processing and a 1 m2antenna from 400 km altitude). This paper presents the instrument and concept advances that have allowed such improved expectations.

The Software PARIS Interferometric Receiver (SPIR) was conceived as a multiple beam-forming GNSS-Reflectometry receiver capable of implementing different signal processing strategies (clean-replica, interferometric, etc.) in order to demonstrate the synoptic capabilities for sea-surface altimetry using the interferometric GNSS-R technique. The instrument has been designed as a 16-channel very-high speed (320 MB/s) hardware recording receiver followed by a software post-processor, implementing the beam-forming and signal processing. The presence of 16 channels together with the beam-forming capabilities requires to characterize the quantization offsets, instrument time-keeping and relative instrumental delays between the different channels in order to have a well-functioning instrument. In this paper we present several calibration aspects of SPIR.

We present in this paper the work done to test the synoptic capabilities of the GNSS-R interferometric technique as a means towards sea surface altimetry estimation, which is an integrated part of the assessment of the GEROS-ISS mission. For this purpose, a new software receiver has been developed and tested: SPIR. With the lessons learned after a first flight campaign, some system modifications were performed and a second experimental campaign has been recently carried on. Initial results show interferometric waveforms that present the quality required for altimetric analysis.

For preventing the effects of sea ice bring the serious negative impact on the maritime transport, a full-scale detection of sea ice should be performed. The BeiDou GEO satellites could provide stable geometry and better coverage in mid- and low-latitude region where most of the sea ice occur. Based on this consideration, this paper evaluates the usage of BeiDou GEO Satellites reflected signals for accurate real-time Earth observation to study the changes in the sea surface state through remote sensing. BeiDou GEO signals received after reflection from Bohai Bay were analyzed for their sea ice content. The results are in good agreement with the cluster center of the sea ice reflected correlation power and the sea water reflected correlation power. The average of the cluster center on the sea ice surface is much smaller than that on the sea water surface.

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring by Global Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced.

This letter focuses on exploiting parameters, including peak power spectral density (PSD), integrated power, mean frequency, and spectrum width of reflected BeiDou signals in the frequency domain, to retrieve wind speed. The PSD of reflected signals is estimated after choosing proper parameters of the Welch method. Then, the features of PSD are illustrated, and a method is proposed based on fitting estimated PSD with a Gaussian function to evaluate the aforementioned PSD parameters. The estimated parameters of collected BeiDou medium earth orbit (MEO)/inclined geosynchronous satellite orbit (IGSO) and geostationary orbit (GEO) data are fitted with in situ wind speed using polynomial. Fitting results show that the peak PSD, mean frequency, and spectrum width of reflected signals from GEO have more evident dependence on wind speed, compared with MEO/IGSO. To obtain the most accurate results, the impact of delay used in lagging direct replica to align reflected signals is analyzed. Results show that regardless of delay locating within the interval of [τ0- τc, τ0+ τc], the better root-mean-square error (rmse) of less than 1.7 m/s and the larger coefficient of determination over 0.8 can be obtained by retrieving from the spectrum width, compared with peak PSD and mean frequency, whose optimal results, with rmse values of 2.03 and 1.70 m/s and coefficient of determination values of 0.73 and 0.81, are obtained as delay is τ0- 0.85τcand τ0+ 0.9τc, respectively, where τ0is the delay of specular reflection, and τcis the length of the B1 code.

A number of applications have been proposed by using the technique of global navigation satellite system reflectometry (GNSS-R) such as ice monitoring and sea surface wind retrieval. Using GNSS-R for target detection has also been raised for its advantages of all-weather capabilities, worldwide coverage and the ability of counter the attack of anti-radiation missiles (ARM). In this work, ocean surface target detection and positioning by this technique is discussed. The analysis mainly focuses on two aspects: 1) the detecting principle and the ability of this approach, and 2) the target positioning method based on the corresponding relation between Delay-Doppler points and the spatial points of the observing area. The positioning ambiguity is also taken into account. The performance of the proposed detection and positioning methods are evaluated by some simulations in a realistic scenario.

Typhoon motoring using Global Navigation Satellite System (GNSS) signals is a new application of the GNSS-R technique, with increasing interest in the last years. Examples of that can be the CYGNSS (Cyclone Global Navigation Satellite System) spaceborne mission proposed by NASA, or the TIGRIS (Typhoon Investigation using GNSS-R Interferometric Signals) experiment, conducted in the framework of ESA-China cooperation. This paper focuses on the TIGRIS experiment, presenting the preliminary results obtained using the interferometric GNSS-R (iGNSS-R) technique.

The bistatically reflected global navigation satellite systems (GNSS) signals have become an attractive tool for spaceborne ocean altimetry. The interferometric processing that can exploit the full bandwidth of the available GNSS signals without the knowledge of the actual ranging codes was proposed for the PARIS IoD mission to improve the ranging precision. This letter presents a novel on-board processing method which utilizes the interferometry of partial GNSS signal components to explore a further improvement of the altimetry precision. The scheme of extracting partial signal components and interferometry procedure, for GPS L1 band as an example, are illustrated. The assessment and comparison of achievable altimetric performance of the proposed method including the altimetric sensitivity, the resolution per pulse, the signal-to-noise ratio, and the overall altimetric precision are also introduced. The extension of the proposed method to other existing and planned GNSS systems and signals would guarantee an improvement in the overall performance of the PARIS concept.