Geoid Validation on the Baltic Sea Using Ship-borne GNSS 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.

Abstract. For the azimuth observation to be made at its magnetic observatories routinely, Japan Meteorological Agency (JMA) has adopted a traditional method based on Polaris sighting. Due to its difficulty to implement under overcast weather conditions and to its demand on observers, for overtime work into the evening, we are motivated to seek for an alternative method based on the Global Navigation Satellite Systems (GNSS) observation that might potentially relieve those two disadvantages. An experiment is made at Kakioka to assess the eligibility and effectiveness of the GNSS method for JMA's unmanned observatories, Memambetsu and Kanoya. The GNSS observations themselves achieve as high a precision as approximately 1 arcsec, as far as they are analyzed with Static mode. Derived from the results of GNSS observation and some supplementary horizontal angle measurements, the azimuth of the azimuth mark for the absolute measurement is determined with a precision of a few arcsecond, which is comparable to the azimuth precision achieved by the Polaris sighting. However, we end up with their significant difference by about 10 arcsec. We discuss this discrepancy to be possibly due to a local geoid gradient. The Polaris observation is made with a theodolite tilted in the gravitational direction, also known as the vertical line deviation, whereas the GNSS observations are based on the azimuth of the compliant ellipsoid plane.

This study focuses on the analysis of the dataset currently available over the Indian subcontinent for the calculation of geopotential numbers and orthometric heights, mainly the performance of regional and global geoid models across diverse topographic conditions. Using Global Navigation Satellite System (GNSS) observations from 17 Asia Pacific Regional Geodetic Project (APRGP) stations distributed across plains, plateaus, coastal areas, and complex mountainous regions, we compared six geoid models: three global (EIGEN-6C4, xGM2019e_2159, SGG-UGM-2) and three regionals (IndGG-SH2021, IndGG-CUT2021, and IndGG-LSMSA2021). Interpolated gravity values, geoidal heights, and geodetic coordinates were utilized to derive geopotential numbers, which were further analyzed to estimate orthometric heights and quantify the discrepancies obtained using currently available data. Our results indicate that recent global geoid models, particularly XGM2019e_2159 and SGG-UGM-2, have improved and have consistency with respect to regional geoid models, mainly with IndGG-SH2021 in terms of accuracy and reliability. Stations located in moderate terrain zones such as Bangalore, Delhi, and Hyderabad showed minimal variations (<±15 cm) in orthometric heights, However, significant discrepancies were observed in mountainous regions like Kashmir, where orthometric height differences exceeded ±25 cm, especially with the IndGG-CUT2021 model. Based on these findings, it is strongly recommended to use gravimetric geoid models derived from satellite missions to analyze the strength of regional models that allow to observation of the trend of the results in each model to verify its consistency. Furthermore, obtain absolute gravity data for India’s vertical datum modernization.

Abstract. Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

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.

Due to the low signal power, the Global Navigation Satellite System (GNSS) signal is vulnerable to interference and even cannot be captured or tracked in harsh environments. As an alternative, the Low Earth Orbit (LEO) satellite has been widely used in the navigation field due to the advantages of low cost and strong signals. It is becoming a significant component of the new combined navigation system with GNSS. The combination of an LEO Doppler signal and GNSS observables can improve the positioning accuracy and high-precision positioning convergence time of the GNSS receiver. However, the GNSS signal receiving capability cannot be improved from this data fusion level. We propose a novel assisted structure where GNSS signal acquisition and Doppler tracking are assisted by LEO Doppler positioning. The receiver uses the LEO signal to achieve Doppler positioning firstly. Then, the coarse position with the GNSS navigation messages received from LEO, as well as the estimated clock information, is used to assist in the acquisition and tracking of GNSS. In this way, the GNSS receiver’s sensitivity can get the benefit from this integrated system. The paper presents the structure of the assisted receiver and analyzes the assisted GNSS signal acquisition and carrier tracking performance in detail. Simulation experiments of this assisted structure are carried out to verify its superiority of acquisition and tracking sensitivity in comparison with standalone GNSS receivers. Theoretical analysis and experimental results show that the proposed acquisition method can achieve 90% detection probability at a carrier-to-noise ratio (C/N0) of 15 dB-Hz, which is about 8 dB higher than the conventional acquisition method without assistance; the proposed tracking method can track weak signals of 5 dB-Hz, which is about 4 dB higher than the conventional method. Therefore, this novel LEO-assisted receiver has significantly improved weak signal acquisition and tracking sensitivity.

It is anticipated that the performance of real‐time (RT) GNSS meteorology can be further improved by incorporating observations from multiple Global Navigation Satellite System (GNSS), including GPS, GLONASS, Galileo, and BeiDou. In this paper, an operational RT system for extracting zenith troposphere delay (ZTD) using a modified version of the Precise Point Positioning With Integer and Zero‐difference Ambiguity Resolution Demonstrator (PPP‐WIZARD) was established. GNSS, including GPS, GLONASS, and Galileo, observation streams were processed using RT Precise Point Positioning (PPP) strategy based on RT satellite orbit/clock products from the Centre National d'Etudes Spatiales. An experiment covering 30 days was conducted, in which the observation streams of 20 globally distributed stations were processed. The initialization time and accuracy of the RT troposphere results using single‐system and multisystem observations were evaluated. The effect of PPP ambiguity resolution was also evaluated. Results reveal that RT troposphere estimates based on single‐system observations can both be applied in weather nowcasting, in which the GPS‐only solution is better than the GLONASS‐only solution. The performance can also be improved by PPP ambiguity resolution and utilizing GNSS observations. Specifically, we notice that ambiguity resolution is more effective in improving the accuracy of ZTD, whereas the initialization process can be better accelerated by GNSS observations. Combining all techniques, the RT troposphere results with an average accuracy of about 8 mm in ZTD can be achieved after an initialization process of approximately 8.5 min, which demonstrates superior results for applying GNSS observations and ambiguity resolution for RT meteorological applications.

Global Navigation Satellite System (GNSS) can provide high-precision three-dimensional real-time or quasi-real-time changes of monitoring points automatically in house monitoring applications. However, due to the signal sheltering problem, large observation noise and multipath effects in urban observing environment with dense buildings, ambiguity resolution would be hard, and GNSS accuracy cannot always achieve millimeter level to satisfy the requirement of house monitoring. Static level is a precision instrument for measuring elevation difference and its variations, with a precision up to sub-millimeter level. It could be integrated with GNSS to improve the positioning accuracy in height direction. However, the existing integration of GNSS and static level is mostly on a respective results level. In this study, we proposed a method of integrating GNSS and static level observations tightly to enhance the GNSS positioning performance. The hardware design and integration mathematic model in data processing were introduced, and a group of experiments were carried out to verify the performance in positioning with and without the static level observation constraints. It found that the vertical monitoring measurement results of static level can achieve less than 1 mm. The GNSS ambiguity resolution performance can be improved by incorporating the measurement of static level into GNSS positioning equation as external constraints, and the precision of GNSS float solutions was significantly improved. Finally, the static level constraint can further improve the accuracy of the fixed solution from about 2 cm to better than 2 mm in vertical direction, which is even better than the accuracy in horizontal directions with about 3–6 mm with the static level constraint. The tight combination data processing algorithm can significantly improve the working efficiency, accuracy, and reliability of the application of dangerous house monitoring.

<p><strong>Abstract</strong> Global Navigation Satellite Systems (GNSS), which are fundamental to Positioning, Navigation, and Timing (PNT), play also a critical role for the determination of ITRF and the monitoring of global geodetic parameters, such as geocenter and Earth Rotation Parameters (ERP), thanks to the globally distributed network. The Low Earth Orbit (LEO) satellites with onboard GNSS receivers as moving stations can significantly improve the observation geometry of a ground tracking network, and thus enhance the solutions. Previous studies have demonstrated that integrated processing of ground GNSS stations and LEO satellites can improve the GNSS satellite orbits and the determination of the geocenter, especially for spare networks. In this study, we further investigate the contribution of having five LEO satellites (two GRACE-FO satellites and three SWARM satellites) to a rather robust ground GNSS network, i.e., 120 stations. We focus not only on the GNSS satellite orbits, and the geocenter and ERP, but also on some other parameters like ambiguities. We further investigate the impact of the LEO orbit modelling and the weighting strategies of the LEO observations in order to properly consider the fact that onboard GNSS observations can be modelled more precisely than that from ground stations due to the differences in station environment and atmosphere.</p>

Basal sliding is a key process that controls the ice discharge of ice into the ocean. Understanding this process is essential for improving the reliability of future projections of ice sheet evolution and sea level rise. The basal sliding law, which governs ice-bed interactions, remains a critical yet poorly understood process in ice sheet models. Recent advances in transient calibration techniques, which incorporate time series of observed surface velocity and elevation, have enhanced the ability of ice numerical models to infer basal conditions. However, relying on the same type of observational datasets for both model calibration and validation limits the ability to independently evaluate model performance, particularly for future projections. In this study, we introduce an independent observational dataset: measurements from the Global Navigation Satellite Systems (GNSS) collected by Greenland GNSS Network (GNET) stations located along Greenland's coastline. By comparing observed uplift signals with modeled mass change, we can validate model behavior and identify sliding laws most consistent with GNSS data. Here, we illustrate this approach by modeling Helheim and Jakobshavn Glaciers from 2007 to 2022 using three different sliding laws. While all sliding laws produce similar surface velocity patterns consistent with InSAR-derived velocity observations, the patterns of mass change, which control bed uplift, differ significantly. Our analysis reveals that all three sliding laws can reproduce uplift signals consistent with GNSS measurements in terms of inter-annual variability. However, only coulomb-limited sliding laws generate uplift signals consistent with GNSS measurements in multi-annual trends. These results highlight the importance of incorporating multiple independent observational data, such as GNSS, into ice sheet models to refine our understanding of basal sliding laws and reduce uncertainties in predicting future sea level rise.

In global navigation satellite system (GNSS)-based positioning and applications, multipath is by far the most obstinate impact. To overcome paradoxical issues faced by current processing approaches for multipath, this paper employs an intrinsic method to identify and mitigate multipath based on empirical mode decomposition (EMD) and Hilbert–Huang transform (HHT). Frequency spectrum and power spectrum are comprehensively employed to identify and extract multipath from complex data series composed by combined GNSS observations. To systematically inspect the multipath from both code range and carrier phase, typical kinds of combinations of the GNSS observations including the code minus phase (CMP), differential correction (DC), and double differential (DD) carrier phase are selected for the suggested intrinsic approach to recognize and mitigate multipath under typical positioning modes. Compared with other current processing algorithms, the proposed methodology can deal with multipath under normal positioning modes without recourse to the conditions that satellite orbits are accurately repeated and surrounding environments of observing sites remain intact. The method can adaptively extract and eliminate multipath from solely the GNSS observations using intrinsic decomposition mechanism. From theoretical discussions and validating tests, it is found that both code and carrier phase multipath can be identified and distinguished from ionospheric delay and other impacts using the EMD based techniques. The resultant positioning accuracy is therefore improved to an obvious extent after the removal of the multipath. Overall, the proposed method can form an extensive and sound technical frame to enhance localization accuracy under typical GNSS positioning modes and harsh multipath environments.

Spacecraft orbit propagator integration with GNSS in a simulated scenario

The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the artificial neutral network (ANN) to construct the correlation between ZTD derived from GPT3 and GNSS observations. The experiments in Hong Kong using Satellite Positioning Reference Station Network (SatRet) were conducted and three statistical values, i.e., bias, root mean square error (RMSE), and compound relative error (CRE) were adopted for our comparisons. Numerical results showed that the proposed model outperformed the parameter ZTD model (Saastamoinen model) and the empirical ZTD model (GPT3 model), with an approximately 56%/52% and 52%/37% RMSE improvement in the internal and external accuracy verification, respectively. Moreover, the proposed method effectively improved the systematic deviation of GPT3 model and achieved better ZTD estimation in both rainy and rainless conditions.

Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various regions within landslides. The present study proposes a landslide spatiotemporal displacement forecasting model by introducing attention-based deep learning algorithms based on spatiotemporal analysis. The Maximal Information Coefficient (MIC) approach is employed to quantify the spatial and temporal correlations within the daily data of Global Navigation Satellite System (GNSS) observations. Based on the quantitative spatiotemporal analysis, the proposed prediction model combines a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture spatial and temporal dependencies individually. Spatial–temporal attention mechanisms are implemented to optimize the model. Additionally, we develop a single-point prediction model using LSTM and a multiple-point prediction model using the CNN-LSTM without an attention mechanism to compare the forecasting capabilities of the attention-based CNN-LSTM model. The Outang landslide in the Three Gorges Reservoir Area (TGRA), characterized by a large and active landslide equipped with an advanced monitoring system, is taken as a studied case. The temporal MIC results shed light on the response times of monitored daily displacement to external factors, showing a lagging duration of between 10 and 50 days. The spatial MIC results indicate mutual influence among different locations within the landslide, particularly in the case of nearby sites experiencing significant deformation. The attention-based CNN-LSTM model demonstrates an impressive predictive performance across six monitoring stations within the Outang landslide area. Notably, it achieves a remarkable maximum coefficient of determination (R2) value of 0.9989, accompanied by minimum values for root mean squared error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE), specifically, 1.18 mm, 0.99 mm, and 0.33%, respectively. The proposed model excels in predicting displacements at all six monitoring points, whereas other models demonstrate strong performance at specific individual stations but lack consistent performance across all stations. This study, involving quantitative deformation characteristics analysis and spatiotemporal displacement prediction, holds promising potential for a more profound understanding of landslide evolution and a significant contribution to reducing landslide risk.

The central hypothesis of the Research Unit (RU) New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV), funded for the second three years phase by the German Research Foundation DFG, reads: only by concurrently improving and better understanding of sensor data, background models, and processing strategies of satellite gravimetry, the resolution, accuracy, and long-term consistency of mass transport series from satellite gravimetry can be significantly increased; and only in that case the potential of future technological sensor developments can be fully exploited.In continuation of the first RU phase, the individual project Improved Stochastic Modeling in GRACE/GRACE-FO Real Data Processing (ISTORE) 2 works towards the completion of the optimized stochastic modeling for GRACE and GRACE-FO gravity field determination. This includes three main tasks: (1) the extension of the stochastic instrument error models of the first phase including Global Navigation Satellite System (GNSS) observations; (2) the optimization of the combination of the different observations; (3) the inclusion of tidal and temporally changing non-tidal background model error variance-covariance matrices in the adjustment process. Based on recent developments within NEROGRAV we also work on a future GFZ GRACE/GRACE-FO Level-2 data release.This presentation provides an overview of the main outcomes of the advanced processing strategies. We will discuss details on stochastic modeling of accelerometer, inter-satellite ranging, and GNSS observations, and of ocean tide and non-tidal atmospheric-oceanic background models. We present Level-2 results based on the monthly solutions within the three test years 2007, 2014 and 2019 in the spectral and spatial domain in comparison with the standard GFZ GRACE/GRACE-FO RL06 time series.