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Global Navigation Satellite System (GNSS) has drawn the attention of scientists and users all over the world for its wide-ranging Earth observations and applications. Since the end of May 2022, more than 130 satellites are available for fully global operational satellite navigation systems, such as BeiDou Navigation Satellite System (BDS), Galileo, GLONASS and GPS, which have been widely used in positioning, navigation, and timing (PNT), e.g., precise orbit determination and location-based services. Recently, the refracted, reflected, and scattered signals from GNSS can remotely sense the Earth’s surface and atmosphere with potential applications in environmental remote sensing. In this paper, a review of multi-GNSS for Earth Observation and emerging application progress is presented, including GNSS positioning and orbiting, GNSS meteorology, GNSS ionosphere and space weather, GNSS-Reflectometry and GNSS earthquake monitoring, as well as GNSS integrated techniques for land and structural health monitoring. One of the most significant findings from this review is that, nowadays, GNSS is one of the best techniques in the field of Earth observation, not only for traditional positioning applications, but also for integrated remote sensing applications. With continuous improvements and developments in terms of performance, availability, modernization, and hybridizing, multi-GNSS will become a milestone for Earth observations and future applications.

The geodetic Global Navigation Satellite System (GNSS) receiver has been proven to retrieve snow depth using the phase change rate of the signal‐to‐noise ratio (SNR) observations. Snow density can be related to snow permittivity and is theoretically sensitive to the amplitude of the GNSS reflected signal. However, retrieving snow density using the SNR observations is challenging due to the difficulty in extracting the reflected amplitude since it hides in the interference waveform and changes with the satellite elevation angle. Overcoming this issue by taking an indirect path, this study proposes a novel GNSS Signal Amplitude Ratio Model (GSARM) that relates the corrected amplitude ratio (α$\\alpha $ ) to the snow permittivity and the resulting snow density. First, the model extracts the instantaneous amplitude from SNR observations to derive an initial amplitude ratio (α0${\\alpha }_{0}$ ). Then, it uses a step‐wise strategy to correct systematic errors from antenna gain and random errors from soil moisture in α0${\\alpha }_{0}$to achieve the finalized corrected α$\\alpha $ . The GSARM‐derived dry snow density is compared with three other data sources, that is, the PBO‐H2O, the ERA5‐Land, and the in situ measurements over two GNSS sites for six consecutive years. The overall mean RMSD (RMSPD) values of snow density for GSARM compared to PBO‐H2O, ERA5‐Land, and in situ measurements are 0.036 g/cm3 (22.08%), 0.040 g/cm3 (21.43%), and 0.032 g/cm3 (23.05%), respectively. The corresponding MAD (MAPD) values are 0.029 g/cm3 (21.90%), 0.035 g/cm3 (18.46%), and 0.025 g/cm3 (22.87%), respectively. The findings of this study first prove the feasibility of using geodetic GNSS receivers for snow density retrieval. It also provides supportive information for extending the added‐value applications of traditional geodetic GNSS sites and for developing new observation patterns. Key Points A first attempt to retrieve dry snow density from geodetic Global Navigation Satellite System (GNSS) receivers A novel GNSS Signal Amplitude Ratio Model (GSARM) is proposed to realize a physics‐based retrieval The geodesy‐dedicated GNSS receiver is feasible while it has inherent limitations, and new observation patterns are required

The Aeolian Archipelago, located in the southern margin of the Tyrrhenian Sea, is a key area to investigate the interplay between regional active fault systems and volcanic activity, making it a focal point for geodynamic studies. In particular, Salina Island lies at the intersection of two major tectonic structures: the Sisifo-Alicudi fault system in the western sector and the Aeolian-Tindari-Letojanni fault system in the central sector both exert a significant influence on the region's deformation patterns. Detecting these signals requires high-quality GNSS data, yet the performance of newly installed stations in tectonic environments must be rigorously assessed. Between June 2023 and February 2024, a new continuous local GNSS network, which consists of five stations, Salin@Net, was established, on Salina Island. The central scientific objective of this study is to verify whether the new GNSS network achieves the data quality necessary for reliable geodetic monitoring and to evaluate its potential to resolve strain gradients in the area. We performed an extensive performance analysis of Salin@net GNSS stations, analyzing data quality, encompassing assessments of multipath effect, signal-to-noise ratio, observation continuity, and cycle slip occurrences, alongside GNSS position time series. These metrics were compared against the ISAL-RING station and benchmarked International GNSS Service (IGS) standards. Results show that the newly installed stations consistently meet the required standards, delivering robust and reliable measurements that are comparable to those of the RING GNSS continuous network. Positioning time series, processed in the ITRF14, indicate that the precision of the derived velocity estimates is comparable to that of standard continuous stations, although longer time spans are required to better constrain linear velocity estimates. Finally, spherical wavelet analysis demonstrates that the geometry of Salin@net significantly improves the spatial resolution of the strain field across the Aeolian-Tindari-Letojanni fault system and enhances resolution along the Sisifo-Alicudi fault, underscoring the role of dense, small-aperture GNSS networks in tectonic environment.

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather.

As global climate change intensifies, hurricane‐induced storm surges are becoming more frequent and severe. While Global Navigation Satellite System‐Interferometric Reflectometry (GNSS‐IR) is widely used to monitor sea level variations, its capability to detect rapid and extreme events remains limited. We propose a short‐time feature extraction GNSS‐IR strategy constrained by astronomical tidal models. By analyzing the continuity and stability of spectral reflections, the method identifies coherent signals from transient sea level changes and effectively addresses the typical 10–20 min temporal bias introduced by the static‐surface assumption. Validation results show that the method achieves a long‐term monitoring accuracy of 4.6 cm over 1 year, and maintains a stable accuracy of approximately 10 cm during storm surges. It also achieves 4.0 cm accuracy over 12‐hr period and enables short‐term sea level prediction with an accuracy of 7 cm. These findings highlight the potential of near‐shore GNSS‐IR to strengthen tide gauge networks and marine assessments.

As seen from comparisons of recent multi-GNSS experiment and pilot project (MGEX) clock solutions of November 23, 2018, and February 15, 2019, most Galileo satellite passive hydrogen maser (PHM) clocks, once corrected for daily drifts, can already show the small error of the conventional relativity correction (i.e., − 2 r.v/c2—negative twice the dot product of the satellite position and velocity vectors, divided by the light velocity squared). The Galileo satellite clock solutions, generated by the GRG and COD MGEX Analysis Centers, in most cases agree well with the simple analytical error approximation, based only on the most significant J2 term of the earth’s gravitational potential expansion. The analytical error approximation is periodical with half an orbit period, and for the Galileo satellites it has an amplitude of 0.064 ns. It is subjected to residual periodical errors with amplitudes of up to 0.025 ns, which also have about half an orbital period. The residual periodical errors are likely caused by orbit perturbations, mostly due to the lunar attraction, which were neglected in the development of the analytical error approximation. Nevertheless, the application of the analytical error approximation to Galileo satellite clocks, corrected for daily drift, resulted in a significant reduction in clock RMS, from the average of 0.095–0.083 ns for GRG MGEX clock solutions on November 23, 2018, and from 0.084 to 0.075 ns on February 15, 2019, for COD MGEX clock solutions. The analytical relativity error approximation improves the linearity of Galileo PHM clocks, which, when accounted for, can result in significant improvements in clock solution precision, clock distributions and predictions. Furthermore, unlike for GPS and GLONASS, Galileo satellite clocks with samplings of 5 or 15 min can be safely interpolated, as demonstrated by Galileo-only kinematic PPP solutions with the MGEX tracking data of Feb. 15, 2019, at the station BRUX. When using the interpolated COD and GRG MGEX SP3 satellite clocks, rather than the 30-s clocks included in the respective clock files, the North/East/Up repeatability sigma’s of 10/8/16 mm increased by less than 10% for COD 5-min SP3 clock sampling. In the case of GRG’s 15-min sampling, the repeatability sigma’s of 8/8/14 mm increased by less than 20%.

The validity of the results obtained within different permanent GNSS reference station networks (GNSS Network) must be periodically controlled using criteria that are generally known from statistical analyzes or prescribed by International Standards. Procedures for evaluating the uncertainty of measurements are defined in accordance with the purpose of the GNSS Network. The authors of this paper want to point out the need to establish requirements for periodical and systematical control of GNSS coordinates within the same permanent GNSS Network and control of GNSS coordinates between different permanent GNSS Networks measured on the same/unique point on the ground. This paper presents control procedures for three permanent GNSS reference station Networks established and operating in the Republic of Serbia. Special attention is on the analysis of data consistency within one permanent GNSS Network and the mutual consistency of GNSS data between different networks. The paper aims to promote reliance on the different GNSS Networks and contains suggestions on how GNSS Networks may prove that they are performing competently and that they can provide valid results for field measurements. Particularly highlighted is the need to plan and implement measures related to increasing the effectiveness of the GNSS system, achieving improved results, and preventing negative effects while performing field measurements. The paper presents the results for comparison, selected according to the rules for creating a Digital Cadastral Map features, i.e., points, lines, and polygon. The results for comparing point features are the GNSS coordinates. The results for comparing line features are the lengths of the line, i.e., distances, and the results for comparing polygon features are the areas of the polygons.

Over the past two decades, low-cost single-frequency Global Navigation Satellite System (GNSS) receivers have been used in numerous engineering fields and applications due to their affordability and practicality. However, their main drawback has been the inability to track satellite signals in multiple frequencies, limiting their usage to short baselines only. In recent years, low-cost dual-frequency GNSS receivers equipped with Real-Time-Kinematic (RTK) engines entered the mass market, addressing many of the limitations of single-frequency GNSS receivers. This review article aimed to analyze the observations and positioning quality of low-cost GNSS receivers in different positioning methods. To provide answers to defined research questions, relevant studies on the topic were selected and investigated. From the analyzed studies, it was found that GNSS observations obtained from low-cost GNSS receivers have lower quality compared to geodetic counterparts, however, they can still provide positioning solutions with comparable accuracy in static and kinematic positioning modes, particularly for short baselines. Challenges persist in achieving high positioning accuracy over longer baselines and in adverse conditions, even with dual-frequency GNSS receivers. In the upcoming years, low-cost GNSS technology is expected to become increasingly accessible and widely utilized, effectively meeting the growing demand for positioning and navigation.

The performance of integrated Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) navigation often declines in complex urban environments due to frequent GNSS signal blockages. This poses a significant challenge for autonomous driving applications that require continuous and reliable positioning. To address this limitation, this paper presents a novel hybrid framework that combines a deep learning architecture with an adaptive Kalman Filter. At the core of this framework is a Temporal Convolutional Network and Bidirectional Long Short-Term Memory (TCN-BiLSTM) model, which generates accurate pseudo-GNSS measurements from raw INS data during GNSS outages. These measurements are then fused with the INS data stream using an Adaptive Noise-Regulated Iterated Extended Kalman Filter (ANR-IEKF), which enhances robustness by dynamically estimating and adjusting the process and observation noise statistics in real time. The proposed ANR-IEKF + TCN-BiLSTM framework was validated using a real-world vehicle dataset that encompasses both straight-line and turning scenarios. The results demonstrate its superior performance in positioning accuracy and robustness compared to several baseline models, thereby confirming its effectiveness as a reliable solution for maintaining high-precision navigation in GNSS-denied environments. Validated in 70 s GNSS outage environments, our approach enhances positioning accuracy by over 50% against strong deep learning baselines with errors reduced to roughly 3.4 m.