A single-receiver geometry-free approach to stochastic modeling of multi-frequency GNSS observables

This study proposes a new set of processing procedures based on the strain model and the Kalman filter (SM-Kalman) to obtain high-precision three-dimensional surface deformation time series from interferometric synthetic aperture radar (InSAR) and global navigation satellite system (GNSS) data. Implementing the Kalman filter requires the establishment of state and observation equations. In the time domain, the state equation is generated by fitting the pre-existing deformation time series based on a deformation model containing linear and seasonal terms. In the space domain, the observation equation is established with the assistance of the strain model to realize the spatial combination of InSAR and GNSS observation data at each moment. Benefiting from the application of the Kalman filter, InSAR and GNSS data at different moments can be synchronized. The time and measurement update steps are performed dynamically to generate a 3-D deformation time series with high precision and a high resolution in the temporal and spatial domains. Sentinel-1 SAR and GNSS datasets in the Los Angeles area are used to verify the effectiveness of the proposed method. The datasets include twenty-seven ascending track SAR images, thirty-four descending track SAR images and the daily time series of forty-eight GNSS stations from January 2016 to November 2018. The experimental result demonstrates that the proposed SM-Kalman method can produce high-precision deformation results at the millimeter level and provide two types of 3-D deformation time series with the same temporal resolution as InSAR or GNSS observations according to the needs of users. The new method achieves a high degree of temporal and spatial fusion of GNSS and InSAR data.

The study of climate change is an important field of research. Monitoring of atmospheric variability especially the tropospheric precipitable water vapor (PWV) is a powerful way to investigate climate change. Global navigation satellite systems (GNSS) provide a good tool for studying atmospheric parameters as GNSS signals along its path from the satellites to the ground based receivers suffer a significant delay due to the refractivity of earth’s atmosphere. GNSS signals are not only delayed but also refracted in the neutral atmosphere. The zenith wet delays (ZWD) caused by the troposphere can be estimated during the geodetic processing of GNSS signals. Since the ZWD is tightly correlated to the PWV, GNSS observations can be used to study the changes of PWV. In this study GNSS data from the Egyptian permanent GNSS network (EPGN), International GNSS Service (IGS), EUREF Permanent Network (EPN) and Scripps Orbit and Permanent Array Center (SOPAC), for the years 2013 and 2014, were used. These GNSS stations provided a good geometrical coverage over the respective region. All GNSS data were processed using Bernese V 5.2 software. The needed product from GNSS data processing is the tropospheric zenith total delay (ZTD) over each GNSS site. The ZTD is divided based on physical parameters into zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD). The ZWD is the basic observable used to calculate PWV. The values of the PWV were calculated and its variation over the study area was investigated. The quality of calculated PWV values from GNSS data evaluated against traditional Radiosonde (RS) measurements. The results of GNSS PWV showed good agreement with RS PWV when the PWV values were low, but this agreement became worse at high PWV values as the differences between the two techniques increased. The correlation coefficient between RS PWV and GNSS PWV varied from 0.31 to 0.84. Standard deviation of the differences between RS PWV and GNSS PWV ranged from 2.369 to 5.973 mm. The PWV estimated from GNSS observations had annual cycle, and its cycle in 2013 was different from that in 2014. The PWV differences between the 2 years clarified that the water vapor content over the east Mediterranean in most of 2014 days was higher than that in 2013. Furthermore, PWV variations were noted on both temporal and spatial scales. The highest temporal variation value was 25.41 mm whereas the maximum value of the spatial variation was 19.67 mm. The present study illustrated the importance of using geodetic networks to provide atmospheric information.

Global Navigation Satellite System (GNSS) data can be used in a myriad of ways. The current number of applications exceed by far those originally GNSS was designed for. As an example, the present Special Issue on GNSS Data Processing and Navigation compiles 14 international contributions covering several aspects of GNSS research. This Editorial summarizes the whole special issue grouping the contributions under four different, but related topics.

The Quasi-Zenith Satellite System (QZSS), which offered positioning signals into your navigation system of devices such as cars and smartphones started service as a four-satellite system from November 2018. They are expected a widespread various innovative services with highly precise positioning. In this study, we describe the development of information processing technology in utilizing Global Navigation Satellite System (GNSS) data in view of the trend of human sensing technology on a wide area outdoor environment. The practical case of target is a human behavior of a skier in ski resort which is case of sports field by GNSS data capture experiment. The behavior of the skier is not only when skiing. There are various actions such as taking a break and ride on a ski lift. That is to say, it is necessary to separate skiing data and other data. In order to realize this, we analyzed the features of the raw GNSS data. We described an analysis of three-dimension GNSS data that is latitude, longitude, high data. As a result, we considered characteristic noise detection results of the data, and extracted of the ski lift ride situation about human behavior analysis.

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

Implementing the International GNSS Service 2021+ Strategic Plan: Vision and Use Cases

With the capability to record Global Navigation Satellite Systems (GNSS) data at high-rate, at sampling rates typical for seismological applications, experiments are needed to quantify the response of GNSS to shaking from earthquakes. There have been a few studies that examine the response of GNSS to strong shaking. Recently, Ebinuma and Kato (2012) used a global positioning system (GPS) simulator to electronically test several GNSS receivers and obtain the receiver characteristics at three frequencies: 1, 2, and 5 Hz. The results showed that the amplitude of 5-Hz displacements recorded by the GPS was, depending on the receiver model, between 30 and 125 percent more than the displacement input to the simulator. At low frequencies, the GPS displacement was nearly equal to the input displacement. In addition, Ebinuma and Kato (2012) examined how each receiver model amplified an earthquake displacement record in the 2–8 Hz band. The study discussed here builds on the tests by Ebinuma and Kato (2012), but rather than using electronic simulation, the tests are setup outdoors and closer to actual field installations of GNSS equipment. The authors used a one-dimensional shake table capable of 400 mm of displacement and high acceleration; the shake table also is constrained by a precision linear slider to have very low tilt that would affect inertial sensors. In addition, the stage position can be accurately monitored independent of the GNSS hardware and, importantly, provides a reference to compare with the estimated displacements from the GNSS data. The tests spanned a greater frequency range from 0.2 to 20 Hz and used equipment from three different manufacturers covering five different combinations of receivers and antennas. In addition, the authors have been able to simulate the frequency response of the GNSS equipment using a simple, causal filter. The quality of the filter was tested using additional test data where a step function in displacement was applied to the shake table. The observed displacements from the GNSS data show an overshoot in displacement at the time of the step or transition of the stage. That overshoot was accurately predicted using the filter design derived from the sinusoidal displacement tests.

Temporal mass variations within the Earth’s system can be detected on a regional/global scale using GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On (GRACE-FO) satellite missions’ data, while GNSS (Global Navigation Satellite System) data can be used to detect those variations on a local scale. The aim of this study is to investigate the usefulness of national GNSS CORS (Continuously Operating Reference Stations) networks for the determination of those temporal mass variations and for improving GRACE/GRACE-FO solutions. The area of Poland was chosen as a study area. Temporal variations of equivalent water thickness ΔEWT and vertical deformations of the Earth’s surface Δh were determined at the sites of the ASG-EUPOS (Active Geodetic Network of the European Position Determination System) CORS network using GRACE/GRACE-FO-based GGMs and GNSS data. Moreover, combined solutions of ΔEWT were developed by combining ΔEWT obtained from GNSS data with the corresponding ones determined from GRACE satellite mission data. Strong correlations (correlation coefficients ranging from 0.6 to 0.9) between detrended Δh determined from GRACE/GRACE-FO satellite mission data and the corresponding ones from GNSS data were observed at 93% of the GNSS stations investigated. Furthermore, for the determination of temporal mass variations, GNSS data from CORS network stations provide valuable information complementary to GRACE satellite mission data.

Abstract. The spatiotemporal variations of integrated precipitable water vapor (IPWV) are very important in understanding the regional variability of water vapor. Traditional in situ measurements of IPWV in the Indian region are limited, and therefore the performance of satellite and Copernicus Atmosphere Meteorological Service (CAMS) retrievals with the Indian Global Navigation Satellite System (GNSS) as reference were analyzed. In this study the CAMS reanalysis data of 1 year (2018) and the Indian GNSS and INSAT-3DR sounder retrieval data for 1.5 years (January 2017 to June 2018) were utilized, and statistics were computed. It is noticed that seasonal correlation coefficient (CC) values between INSAT-3DR and Indian GNSS data mainly lie within the range of 0.50 to 0.98 for all the selected 19 stations except Thiruvananthapuram (0.1), Kanyakumari (0.31) and Karaikal (0.15) during the monsoon season and Panjim (0.2) during the post-monsoon season. The seasonal CC values between CAMS and GNSS IPWV range from 0.73 to .99 except for Jaipur (0.16) and Bhubaneswar (0.29) during the pre-monsoon season, Panjim (0.38) during the monsoon season, Nagpur (0.50) during the post-monsoon season, and Dibrugarh (0.49) Jaipur (0.58) and Bhubaneswar (0.16) during the winter season. The root mean square error (RMSE) values are higher under the wet conditions (pre-monsoon and monsoon season) than under dry conditions (post-monsoon and winter season), and we found differences in magnitude and sign of bias for INSAT-3DR and CAMS with respect to GNSS IPWV from station to station and season to season. This study will help to improve understanding and utilization of CAMS and INSAT-3DR data more effectively along with GNSS data over land, coastal and desert locations in terms of the seasonal flow of IPWV, which is an essential integrated variable in forecasting applications.

A wide-range utilisation of Global Navigation Satellite Systems (GNSS) across technology and socioeconomic domains renders the satellite navigation one of the pillars of the modern civilisation. Tackling and overcoming The inherent shortcomings and emerging threats to provision of robust and resilient GNSS Positioning, Navigation and Timing (PNT) services have become a research subject of the utmost importance. An open access to the GNSS positioning estimation process is fundamental for development of advanced methods for robust and resilient GNSS position estimation. Recent introduction of Google Android 7.0 revision allows for direct access to raw GNSS pseudoranges observed by smartphones. Here we address the opportunities for GNSS positioning estimation improvements, given through exploitation of Google Android Location API, in creation of bespoke GNSS position estimation process in navigation application domain of a GNSS Software-Defined Radio (SDR) receivers in smartphones. We present and validate opportunities for improvement of GNSS position estimation process through utilisation of distributed computing architectures, trusted sources of GNSS augmentation data and utilisation of GNSS positioning methods suitable for targeted classes of GNSS applications Authors are members of European GNSS Agency's GNSS Raw Measurements Task Force, and co-authored the white-paper on GNSS-related Google Android Location API applications.

The Wadi Hagul region in the eastern desert of Egypt is facing seismic hazards and increased human activity. This study uses remote sensing and geodetic methods to monitor and analyze recent deformation in the area. Interferometric Synthetic Aperture Radar (InSAR) data from the Sentinel-1A satellite and Global Navigation Satellite System (GNSS) data were combined to track surface movements and deformations accurately. The study analyzed InSAR data from February 4, 2020, to February 07, 2024, and GNSS data from the Wadi Hagul geodetic network established in July 2022 and monitored until January 2024. Despite the relatively short GNSS monitoring period, it provided valuable insights into recent deformation trends. By integrating data from ten GNSS stations, including International Geodetic stations (IGS), and InSAR scenes from the Sentinel-1A mission, the study estimated recent ground deformation in the region. The main objectives were to analyze recent crustal movements by identifying spatial and temporal patterns of deformation and assess implications for geological processes. In Key Findings, horizontal movement fluctuates between 0.5 and 2.5 ± 0.1 mm annually across the geodetic network. The estimated velocity of the area was 1.5–2 ± 0.5 mm per year. Integrating GNSS and InSAR data helped calculate movement rates along fault lines and create a fault map. In conclusion, the results suggest that while current deformation rates are moderate, they could increase significantly due to human activity, leading to higher seismic activity and potential earthquakes. Limiting human activity in the region is advisable to prevent negative impacts on nearby populated areas.

Global Navigation Satellite System (GNSS) is an established atmospheric observing system for atmospheric water vapour with high spatiotemporal resolution. Water vapour is under-sampled in the current meteorological and climate-observing systems, and thus obtaining and exploiting more high-quality humidity observations is essential.  The operational GNSS data processing at GFZ provides all tropospheric products, zenith total delays (ZTD), precipitable water vapour (PWV), slant total delays (STD) and tropospheric gradients in the framework of different meteorological projects like e.g. E-GVAP ("The EUMETNET EIG GNSS Water Vapour Programme", http://egvap.dmi). E-GVAP is in charge of the collection and quality control of operational GNSS tropospheric products for numerical weather prediction. GFZ is one of the E-GVAP Analysis Centres and processes about 600 GNSS stations in near real-time. The tropospheric products provided by GFZ are used by European weather services for operational forecasts.  GFZ contributes to climate research within the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN). Established in 2006, GRUAN, is an international reference observing network of sites measuring essential climate variables above the Earth's surface. Currently, this network comprises 33 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity. GFZ hosts a central processing facility for the GNSS data and is responsible for the installation of GNSS hardware, data transfer, processing and archiving, as well as derivation of GNSS-PWV products according to the GRUAN requirements. A complementary small scale regional climate station network is the Austrian WegenerNet, which provides since 2007 measurements of hydrometeorological variables with very high spatial and temporal resolution. GNSS-PWV measurements have been included as a priority one measurement of the essential climate variable water vapour to both GRUAN and WegenerNet climate station networks.GNSS-derived tropospheric products and results of selected validation studies will be presented.

Precise timing and precise location information are provided by Global Navigation Satellite Systems (GNSS) and play a crucial role in the positioning, navigation and data acquisition of most Unmanned Aerial Vehicles (UAV). GNSS functions include the following applications in UAVs: time-stamping and geo-referencing of collected data and images, synchronization of swarm flying and follow-me flights, determination of position and attitude in-flight, flight trajectory by following a pre-defined number of waypoints, mission planning, return home automatically without external control, avoidance of obstacles and geo-fencing. 
 Some of these critical operations have implications for the safety of the UAV, the surrounding environment and health and safety of people, for example UAVs threatening to bring down aircrafts at airports, which are no-fly zones for UAVs. The appropriate GNSS based function to avoid this is geo-fencing. Another example is obstacle avoidance to prevent collisions and damages both for the UAV and the obstacle, e.g. anything from a window pane, tree, human being, to a power line.
 In order to ensure health and safety it is thus important to ensure correct function of the navigation and the timing, under a wide variety of circumstances, and in different signal environments. There can be signal disturbances, such as obscurations by buildings or reflected GNSS signals, called multipath. The performance of timing and navigation based on GPS/GNSS can be tested and verified in a controlled and repeatable way in the laboratory with different types of test equipment. We will give an introduction to a wide range of potential threats to GNSS Positioning, navigation and timing and an overview of different test methods. In addition, we are presenting a method for time synchronization of drones to enable safe swarm and follow flights in UAVs.

The Choushui River alluvial fan is an important agricultural production region in western Taiwan. This insufficient level of rainfall replenishment, combined with the unauthorized use of wells to irrigate crops, exacerbated land subsidence in the region. In this study, levelling, global navigation satellite system (GNSS), and interferometric synthetic aperture radar (InSAR) observation data were collected from 2018 to 2021 for analysis. The GNSS data were processed using the precise point positioning method, and the results were compared with data processed using the traditional static relative positioning method. The 3-year cumulative subsidence at the subsidence centre was approximately 9 cm in Changhua and 19 cm in Yunlin. To compensate for the lack of spatial resolution of GNSS and improve the accuracy of InSAR, the InSAR, GNSS, and levelling data were integrated, and these results were validated against levelling check points. They showed an average error of 0.4 cm/year in the annual subsidence rate. Overall, the GNSS had high precision and continuity, making it suitable for real-time early warning of land subsidence. InSAR obtained data with higher spatial resolution, allowing for early detection of new land subsidence areas and comprehensive monitoring of changes in land subsidence across the entire region.

The gravity recovery and climate experiment (GRACE) and GRACE follow-on (GFO) gravity observations have significantly improved our understanding of the terrestrial water cycle. However, GRACE-assimilated (GA) hydrological models still differ significantly. This paper uses global navigation satellite system (GNSS) data to assess two global GA datasets: Global land water storage release 2 (GLWS2.0) and catchment land surface model GRACE data assimilation (CLSM-DA). From 2004 to 2019, the mean annual amplitude of equivalent water thickness (EWT) of these datasets differs by more than 25mm over 40% of the modeled land area, and the timing of peak water storage diverges by as much as 30-days across 50% of their domain. We compare the modeled hydrological loading vertical displacement predicted from these models with GNSS uplift data to compare and contrast the model quality. Using river basin boundary information from 89 rivers, we cluster 9,163 global GNSS stations, each with at least three years of daily data. Results show that CLSM-DA generally agrees better with GNSS data across more river basins. Its 100-300mm larger annual water variation accounts for better agreement in Africa, Southeast Asia, and parts of South America. In regions like the Western United States and Eastern Europe, where both models estimate similar annual amplitudes, CLSM-DA's 30-60day phase delay improves alignment with GNSS. This evaluation also reveals key limitations in both models, especially during extreme hydrological events such as droughts, and highlights the value of geodetic observations in advancing GA hydrological modeling.