A GNSS station, located on the shore of sea and inland waters, and equipped with standard geodetic receivers and antennas, can be used to measure water levels using a technique called GNSS Interferometric Reflectometry (GNSS-IR). The classical GNSS-IR method is based on SNR data and LSP spectrum analysis method. In order to promote the application of GNSS-IR, the accuracy of the results needs to be further improved, and quality control needs to be achieved better. Classical quality control methods include denoising filtering based on data source SNR; post-processing filtering based on results; morphological analysis based on parameters, such as the ratio of the maximum peak value to the background noise mean, the ratio of the maximum peak to the sub-peak, and the amplitude of the maximum peak. All three methods have the problem of correct frequency extraction under multiple approximate peak conditions. This paper focuses on the performance analysis of three methods of quality control for two situations with real examples, summarizes the advantages and disadvantages of each method, and discusses the measures in applications. Considering the limitations in the threshold setting for the third method, a new quality control method combining multiple parameters and external constraints is proposed. This method is more flexible, especially in dealing with a periodogram with multiple similar peaks, breaking through the premise that the frequency corresponding to the maximum peak is the correct frequency, and validated in two different environments. The experimental results show that the proposed method can improve the accuracy of the measured water level while ensuring the amount of the results. It eliminates the gross errors effectively and uses the data efficiently.

This paper discusses the sea level determination using raw Intermediate Frequency (IF) data transmitted from the Global Navigation Satellite System (GNSS) and received by spaceborne GNSS-Reflectometry satellites, TechDemoSat-1. The reflected signals scattered from a sea ice surface and a rough sea surface are investigated. The altimetry method based on the bistatic group delay (code phase) from GNSS signals for sea level estimation are introduced. The two raw IF datasets recorded on January 18 and 27, 2015 for a duration of 40 seconds are analyzed to drain more information than Level 1 data. The results show a good consistence with mean sea surface (MSS) model. The orbit error of the GNSS-R satellite is corrected by a proposed method that combines the MSS and the least squares solution, which help evaluate the actual altimetry precision. The defect of fixed temporal resolution and fixed 4 onboard processing channels of Level 1 data can be improved by postprocessing using Software Define Receiver (SDR) to mine more information, so as to explore the potential. At the end, fake high signal-to-noise ratio (SNR) DDMs from the raw data are analyzed, which provides a reference for the altimetry of GNSS-R technique using raw data.

With the development of global navigation satellite systems (GNSS), it has been demonstrated that signal-to-noise ratio (SNR) observations from geodetic GNSS receivers can be used to estimate the parameters of reflecting surfaces, through a technique called GNSS-interferometry reflectometry (GNSS-IR). Recent studies have revealed that SNR-derived cut-off elevation and damping coefficient have the potential to estimate wind speed. However, the specific mathematical relationship between them and the wind speed remains undetermined. Consequently, this study, based on the existing approximate models for coherent and incoherent components of specular scattering, upgraded an existing SNR simulator to account for incoherent power of scattering signals. This upgraded simulator was used to conduct SNR simulations under varying wind speeds, allowing for the derivation of the relationship between damping coefficient and wind speed, as well as the relationship between cut-off elevation and wind speed. Moreover, an optimal design approach was introduced to estimate the damping coefficient of an SNR arc, preventing divergence in cases of insufficient matrix rank using the least-squares method. Data from GPS L5 signals recorded at the HKQT station during Super Typhoon “Mangkhut” were used. The results indicate that the simulated cut-off elevationes and damping coefficients can reflect the trend of actual parameters varying with wind speed. However, there are still some differences between the simulated data and the actual data. At high wind speeds, the simulated cut-off elevation angles are lower than the actual extracted values, while the simulated damping coefficients are higher than the extracted coefficients.

This paper analyzes the interferometric measurements of ground-based Global Navigation Satellite Systems (GNSS) stations and proposes a novel method for sea surface states detection. The novel technique benefits from a cost-effective data collection from a large number of global GNSS stations. In this study, we extend a traditional GNSS interferometry reflectometry (GNSS-IR) model so that it can be applied to a multilayer surface by considering the surface roughness, total reflectivity, and penetration loss in multilayer situations. Based on this model, the wavelet analysis is used to perform parameterization on the interferometric observations represented by the Signal to Noise Ratio (SNR). An integration factor and power curve are also proposed to characterize the surface state transition. One-year data from an Arctic geodetic GNSS station in the north of Canada are collected for analysis to validate the proposed approach with comparisons to the existing methods based on the amplitude and damping factors. The results show that the new method demonstrates good usability and sensitivity to detect surface state transitions, eg. icing, snowfall, and snow melting. However, the amplitude and damping factor-based methods derived from the single-layer model are only able to detect the pure ice surface and cannot respond to thick snow conditions. Finally, the high-resolution spaceborne images confirm the reliability of this method, exhibiting a great potential for long-term coastal sea surface detection based on the global geodetic GNSS stations and later being expected to be applied to sense cryosphere surface states.

The spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) offers versatile Earth surface observation. While the accuracy of the computed geometry, required for the implementation of the technique, degrades when Earth’s surface topography is complicated, previous studies ignored the effects of the local terrain surrounding the ideal specular point at a suppositional Earth reference surface. The surface slope and its aspect have been confirmed that it can lead to geolocation-related errors in the traditional radar altimetry, which will be even more intensified in tilt observations. In this study, the effect of large-scale slope on the spaceborne GNSS-R technique is investigated. We propose a new geometry computation strategy based on the property of ellipsoid to carry out forward and inverse calculations of path geometries. Moreover, it can be extended to calculate unusual reflected paths over versatile Earth’s topography by taking the surface slope and aspects into account. A simulation considering the slope effects demonstrates potential errors as large as meters to tens kilometers in geolocation and height estimations in the grazing observation condition over slopes. For validation, a single track over the Greenland surface received by the TechDemoSat 1 (TDS-1) satellite with a slope range from 0% to 1% was processed and analyzed. The results show that using the TanDEM-X 90 m Digital Elevation Model (DEM) as a reference, a slope of 0.6% at an elevation angle of 54 degrees can result in a geolocation inaccuracy of 10 km and a height error of 50 m. The proposed method in this study greatly reduces the standard deviation of geolocations of specular points from 4758 m to 367 m, and height retrievals from 28 m to 5.8 m. Applications associated with topography slopes, e.g., cryosphere could benefit from this method.

Significant wave height (SWH) is a crucial parameter that characterizes oceanic wave behavior, playing a pivotal role in oceanic research and disaster management strategies. Currently, the observation of SWH predominantly relies on both buoy and spaceborne microwave remote sensing techniques. However, these techniques face challenges when applied in coastal regions. Addressing this issue, this study introduces an innovative approach leveraging reflected signals to estimate SWH based on coastal Global Navigation Satellite System (GNSS) stations. We establish a model for wave height retrieval by examining the temporal variation in signal-to-noise ratio (SNR) data related to SWH, drawing upon the GNSS interferometric reflectometry (GNSS-IR) method for SWH retrieval. Experimental findings highlight the efficacy of the multisystem GNSS-IR SWH inversion. It demonstrates an accuracy of 12 cm, coupled with an average temporal resolution of 29 min, and exhibits a strong correlation coefficient of 0.95 when compared to ocean buoy measurements. The deployment of coastal GNSS stations emerges as a promising source for obtaining true and reliable data on coastal SWH.