Human activities in the Arctic regions have been increasing in recent years due to the impacts of climate change, such as Arctic Sea ice decline. For example, there has been an increase in Arctic shipping routes. A robust navigation system with a high positioning accuracy is required when traversing the extremely challenging Arctic environment to ensure the safety of human activities. However, the high–precision GNSS navigation and the positioning method, e.g., real–time kinematic (RTK), is not available in the polar regions due to the accessibility issues of the required infrastructures. On the other hand, the International GNSS Service (IGS) enables real–time applications; additionally, quick and convenient satellite communication systems are also available. This offers the possibility of real–time precise point positioning (RT–PPP) with multi–GNSS for high-precision navigation in the Arctic. In our paper, we analyzed the performance of multi–GNSS RT–PPP in the Arctic Northeast Passage (NEP), highlighting the following contributions: First, a GNSS device is installed on the M/V TIANHUI, which passed through the NEP from 10 September to 20 September 2019; Second, we quantitatively evaluated the collected GNSS signals in terms of the maximum satellite elevations, number of visible satellites (NSAT), position dilution of precision (PDOP) values, signal–to–noise ratio (SNR), and multipath errors. Third, we evaluated the accuracy of the CLK93 real–time products compared with the Deutsches GeoForschungsZentrum (GFZ) final products GBM. Finally, we carried out experiments for both single– (SF) and dual–frequency (DF) RT–PPP in the NEP during the 11–day testing period. Our experimental results show that meter–level positioning accuracy can be achieved with SF RT–PPP, while the DF RT–PPP model reaches sub–decimeter values and even centimeter–level accuracy. In addition, using the multi–GNSS method, we showed that the average RMS values of DF RT–PPP in the horizontal and vertical directions are 0.080 m and 0.057 m, respectively, demonstrating an improvement of approximately 70% over single–GPS solutions.

As a novel remote sensing method, Global Navigation Satellite System-Reflectometer (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 porposed 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 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.

Using delay‐Doppler maps of Global Navigation Satellite Systems Reflectometry (GNSS‐R) from the TechDemoSat‐1 satellite and considering sea ice and ocean interaction, an innovative method for retrieval of Arctic sea ice concentration (SIC) based on a deep neural network is proposed. This retrieval method shows the potential of future GNSS‐R applications for Arctic missions. Compared with SIC products from Hamburg University, the root mean square errors (RMSE) of retrieved results in March and June 2016 are 0.0284 and 0.0415, respectively. When the retrieved GNSS‐R SIC data are added into the assimilation as supplementary passive microwave remote‐sensing data, it has a positive influence on improving the accuracy of the Arctic SIC forecast. Especially in some edge regions of sea ice, when compared to only assimilating the remote‐sensing data, the regional RMSE of joint assimilation has a maximum decrease of approximately 17% in the 24‐hr forecast time, and over 5% in 72‐hr.

The GNOS-II 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 parameterss 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 Leveal 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 1.5million co-located 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.

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 paper focuses on the Coherence 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 classes. The proposed approach is straightforward to implement and applicable to current and future high-temporal-resolution GNSS-R missions.