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.

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.

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.

Spaceborne global navigation satellite system reflectometry (GNSS-R) techniques have been developed for sea surface wind speed retrieval in recent years. In order to utilize both the leading edge slope (LES) and normalized bistatic radar cross-section (NBRCS), the minimum variance estimator (MVE) is used in the cyclone GNSS (CYGNSS) algorithm for wind retrieval. However, due to the high correlation of two observables, the root mean square error (RMSE) of the MVE estimated winds is not improved significantly. In this article, a new method by combining retrievals from delay-Doppler map (DDM) observables based on particle swarm optimization (PSO) is proposed. LES and NBRCS observables from CYGNSS V2.1 products are used, and then wind retrievals from them are combined by PSO. In order to validate the performance, European Center for Medium-Range Weather Forecasts (ECMWF) and cross-calibrated multi-platform (CCMP) ocean surface wind vector analysis product 10-m ocean surface wind products are used as ground truth. The results show that, when using ECMWF winds, the RMSE of MVE retrievals is 2.21 m/s, while that of PSO is 1.95 m/s: an improvement of 12%; when using CCMP winds, the RMSE of MVE retrievals is 2.15 m/s, while that of PSO is 1.92 m/s: an improvement of 11%. Therefore, we conclude that the PSO algorithm is an improvement on the state-of-the-art MVE GNSS-R-based wind speed retrieval techniques. However, the PSO-based wind retrievals show the dependence on the GPS block type and the CYGNSS satellite identifier that the MVE-based techniques suffer from.

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.