Using reflected signals from global navigation satellite systems (GNSS) to measure sea surface height is an important application of the GNSS-reflectometry (GNSS-R) technique. The interferometric GNSS-R (iGNSS-R) utilizes the full spectrum of the navigation signal for ocean altimetry, improving precision due to the increased bandwidth compared to conventional GNSS-R (cGNSS-R). The implementation of iGNSS-R relies on the autocorrelation function (ACF) of the full-spectrum navigation signals. However, the unknown power distribution of each signal component complicates the acquisition of the theoretical ACF. In this article, we obtain the ACFs of GPS IIR-M/IIF/III L1, Galileo E1, and BeiDou-3 B1 signals by processing the intermediate-frequency (IF) data from a high-gain directional antenna. These measured ACFs are then applied to spaceborne iGNSS-R simulation to analyze their impact on ocean altimetry, including altimetry sensitivity, precision, and delay correction in altimetric waveform retracking. The simulation results indicate that iGNSS-R achieves submeter-level altimetry precision, with an improvement of$1.31\times $–$7.15\times $compared to cGNSS-R. Moreover, the ACF of the composite GNSS signal can also affect the altimetric waveform retracking. The deviation of the ACFs can induce a significant systematic effect in reflected signal delay estimation, which may cause centimeter- to decimeter-level bias in iGNSS-R altimetry results for different retracking methods. In addition, the reflected waveform varies with changes in the incidence angle and wind speed. While the wind speed and incidence angle have minimal effects on DER, the delay difference of HALF caused by waveform variations increases at higher incidence angles.

Global navigation satellite system reflectometry (GNSS-R) is a passive remote sensing technique that utilizes GNSS signals reflected from the Earth's surface to monitor ocean winds, soil moisture, sea ice, surface water, and vegetation. Reflected GNSS signals consist of both coherent and incoherent components; compared with incoherent scattering, coherent reflections provide stronger signal strength, finer spatial resolution, and stable carrier-phase properties that are essential for high-precision retrievals. However, coherence indicators are highly sensitive to the signal-to-noise ratio (SNR), and under low-SNR conditions, they exhibit systematic compression that degrades coherence detection performance. To address this issue, this article focuses on the coherent coefficient (CC) - the coherence indicator adopted by the upcoming HydroGNSS mission - and proposes a simulation-driven framework to model and compensate for the nonlinear relationship between SNR and CC. The method constructs an empirical SNR-CC mapping through simulations and introduces a dynamic weighting scheme to adaptively correct observed CC values. Validation using CYGNSS complex-waveform data over Qinghai Lake, the Amazon Basin, and the Mississippi River Basin demonstrates that the proposed approach effectively mitigates CC compression in low-SNR regimes. Statistical analyses over the Mississippi River Basin demonstrate that the proposed compensation method improves mean coherence by 0.1-0.2 on average in low-coherence surface, while reducing the standard deviation by 10%-20% across major land cover (LC) classes. The proposed approach is straightforward to implement and applicable to current and future high-temporal-resolution GNSS-R missions.

The GNOS-II global navigation satellite system reflectometry (GNSS-R) Level 1 products from FENGYUN-3E, which include delay-Doppler map (DDM) and normalized bistatic radar cross section (NBRCS), significantly affect the inversion accuracy of geophysical parameters such as sea wind and soil moisture. This study uses data collected from FY-3E GNOS-II GNSS-R and data from ECMWF ERA-5 sea surface wind speed, covering the period from July 2022 to June 2023. In addition, CYGNSSv3.1 Level 1 data are used as a reference to evaluate NBRCS, observation geometry, GNSS satellite types, equivalent isotropic radiated power (EIRP) of GNSS and peak signal-to-noise ratio (SNR). The results show a strong correlation between FENGYUN-3E and approximately 1.5 million colocated cyclone global navigation satellite system (CYGNSS) specular points, with an overall NBRCS correlation coefficient of 0.882. Compared to CYGNSS, the FY-3E/GNOS-II specular reflector point offers broader coverage and the capability to process reflected signals from GPS, BDS, and GALILEO systems. However, FY-3E operates at a higher orbital altitude, resulting in reduced range-corrected gain (RCG), lower peak SNR, and a more scattered distribution of NBRCS. FY-3E successfully achieves stable NBRCS measurements across multiple GNSS systems. However, slight bias in NBRCS values is observed between different GNSS systems due to variations in EIRP calibration methods. These findings provide critical information for the practical application and future calibration of FY-3E Level 1 data.

Sea ice plays a crucial role in global climate patterns, making the acquisition of sea ice change information significant. The rapid development of GNSS and LEO satellites has facilitated the emergence of spaceborne GNSS-Reflectometry (GNSS-R) as a novel remote sensing approach. Previous studies predominantly used Delay-Doppler maps (DDM) to detect sea ice, which posed challenges in identifying small-scale sea ice information due to the sampling rate (1 Hz) of DDM. This paper proposes a method for high along-track spatial sampling resolution detection of sea ice and ice leads using complex waveform (CWF) products for the first time. CWF provides amplitude and phase information at a higher sampling rate (1000 Hz), which can potentially construct high-resolution sea ice detection observables. In this paper, the mean coherence coefficient (MCC) observable with 20 ms temporal resolution is extracted to quantify the difference in residual phase change between sea ice and open ocean. Then, a corresponding MCC observable threshold is established based on the training dataset to realize sea ice detection. The method is validated using TDS-1 CWF with OSISAF SIC datasets as references. The results show detection accuracies of 93.59% and 83.84% for the northern and southern hemispheres, respectively, coupled with a remarkable fifty-fold improvement in along-track spatial sampling resolution. In addition, the method was also used to identify ice leads in Davis Strait. The results revealed a high correlation coefficient of 78.42% between MCC observables and surface reflectance values extracted from MODIS products, preliminarily proving the feasibility and application potential of spaceborne GNSS-R in ice leads detection.

Tropospheric delay is an essential parameter in climate research and a crucial factor for enabling high-accuracy satellite geodesy. If shipborne Global Navigation Satellite System (GNSS) zenith total delay (ZTD) retrieval can be implemented, it will effectively compensate for the lack of ground-based stations in marine areas and is a potentially rich data source. In this study, we demonstrates the ZTD retrieval at sea using PPP method with a shipborne low-cost multi-GNSS receiver installed onboard the M/V TIANHUI, which completed a three-month voyage in the Northern Hemisphere. The experimental results showed that, there is good consistency between the shipborne GNSS ZTD and the nearby ground-based GNSS stations ZTD, whether the ship is anchored or sailing. For the ship route comparison, the ZTD retrieval from shipborne GNSS and ERA5 reanalysis data are in good agreement, with a correlation coefficient of 93.86%, BIAS and RMS values of 0.75 cm and 2.65 cm, respectively. The field tests demonstrate that the potential of shipborne low-cost multi-GNSS receivers to retrieve meteorological parameters, providing the feasibility for future large-scale shipborne detection of meteorological parameters. Furthermore, this also provides tremendous data for the ocean without sufficient ground-base stations/Radiosonde stations in the future.

As a novel remote-sensing method, the Global Navigation Satellite System-Reflectometry (GNSS-R) can utilize a large number of reflected GNSS opportunity signals for sea ice observation. The first analysis of spaceborne GNSS-R data from the Chinese Jilin-1 Wideband-01B (J1-01B) Satellite Mission is carried out. 72 days of delay-Doppler maps (DDMs) obtained from global positioning system-reflectometry (GPS-R) and BeiDou navigation satellite system-reflectometry (BDS-R) is utilized for sea ice detection. In addition to the traditional pixel number (pn) and power summation (PS) observables, a novel DDM observable, named “trailing edge diffusion (TED),” was proposed to quantify the significant differences observed in DDMs obtained from seawater and sea ice. By establishing corresponding DDM observable thresholds, sea ice can be distinguished from seawater. Compared to the ocean and sea ice satellite application facility (OSI SAF) global sea ice concentration (SIC) product, the agreement of GPS-R and BDS-R is more than 96% and 98%, respectively, in the Antarctic. Results show high accuracy in the sea ice detection field, which demonstrates the feasibility of J1-01B DDMs and the superior performance of GPS-R and BDS-R in detecting sea ice. This study provides a novel data source for the cost-effective, data-rich, and stable GNSS-R method in sea ice detection.

In the Global Navigation Satellite System reflectometry (GNSS-R), the spectral analysis approach is widely used to derive sea level height from signal-to-noise ratio (SNR) data because of its simplicity and ease of implementation. However, it requires many corrections to improve accuracy such as data quality control, outlier removal, and SNR bias correction. Moreover, the correction methods are normally post-processing, which are not suitable for real-time estimation. In this paper, we propose a novel method using a combination of two neural networks, including the SNR Forward Network and Time-aware Long Short-Term Memory Model Anomaly Detection (T-LSTM-AD), to improve the spectral analysis approach to be more accurate and closer to real-time estimation. SNR Forward Network is the special neural network designed based on the SNR physical model with consideration of the surface roughness term. It learns SNR biases from historical SNR data and is applied to new SNR data for SNR biases correction without using other external information. T-LSTM-AD is a new approach for outlier detection and dynamic surface modeling based on the temporal dependency of data. The trained T-LSTM-AD is used to classify the outlier and correct the dynamic surface for the new estimated sea level. To verify the performance, 1-year data from GTGU is separated to the training and testing data. The results of testing data with 6.3 cm RMSE and 0.939 correlation coefficient show that the proposed method has good performance. Furthermore, when applied to 1-year data, the proposed method provides accurate results compared to the existing methods.