This session is a forum to discuss advances in high-precision GNSS algorithms and their applications in geosciences such as geodesy, geodynamics, seismology, tsunamis, ionosphere, troposphere, etc.
We encourage but do not limit submissions related to:
- GNSS processing algorithms,
- Multi-GNSS benefits for Geosciences,
- Multi-constellation GNSS processing and product standards,
- High-rate GNSS,
- Low-cost receiver and smartphone GNSS observations for precise positioning, navigation, and geoscience applications,
- LEO-augmented precise positioning and quality control,
- LEO observations modeling and processing algorithms,
- Precise Point Positioning (PPP, PPP-RTK) and Real Time Kinematic (RTK),
- GNSS and other sensors (accelerometers, INS, etc.) fusion,
- GNSS products for high-precision applications (orbits, clocks, uncalibrated phase delays, inter-system and inter-frequency biases, etc.),
- Troposphere and ionosphere modeling and sounding with GNSS,
- CORS services for Geosciences (GBAS, Network-RTK, etc.),
- Precise Positioning of EOS platforms,
- GNSS for natural hazards prevention,
- Monitoring crustal deformation and the seismic cycle of active faults,
- GNSS and early-warning systems,
- GNSS reflectometry.
Differential Code Biases (DCBs) are systematic errors, or biases, between two Global Navigation Satellite System (GNSS) code observations at the same or different frequencies. Knowledge of DCBs is required for positioning of GNSS receivers, extracting ionosphere total electron content (TEC), and other applications. Proper knowledge of DCBs is crucial to many navigation applications but also non-navigation applications such as ionospheric analysis and time transfer. The multitude of new signals offered by modernized and new GNSS constellations, requires a comprehensive multi-GNSS bias product. And because of the multitude of GNSS signals it has become more practical to provide the DCBs, differential signal biases, as observable specific biases, OSBs.
At the Navigation Support Office at ESA/ESOC we have for many years relied on the bias products (DCB and OSB) coming from the CODE analysis centre. But in the fast-developing Multi-GNSS landscape it became clear that we needed to have to capability to generate our own independent bias product. For our different GNSS projects we therefore have developed a process to generate our own OSB product with the ambition to have DCBs for all existing MGNSS signals, as long as the signals are tracked by the stations in the large IGS station network. And rather than monthly biases as typically provided and used within the IGS, we have moved to what we call a “Bias Reference Frame” (BREF). The absolute value is still determined by a zero-mean condition at a certain date. But from that point in time the biases are kept stable unless a clear jump in the satellite bias is, automatically, detected. The detectability is at the 0.25ns for well observed biases. The DCB version of the product is publicly available on our Navigation Office website. The OSB version is still under development and testing.
In developing this product, we found some very interesting features of the biases when looking at the biases of the so called “interoperable” signals, which we will present and discuss. We demonstrate the importance of this product for the analysis of the Sentinel 6A data which tracks Galileo and an interesting mix of GPS signals which makes it hard to process the GPS data with the standard IGS products, in particular for PPP-AR.
How to cite: Springer, T., Otten, M., Mayer, V., Gini, F., and Schönemann, E.: Bye-bye, bias! The ESA Multi-GNSS Bias Reference Frame, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19887, https://doi.org/10.5194/egusphere-egu26-19887, 2026.
The zero-difference (ZD) and double difference (DD) approach for processing GNSS observations is mathematically equivalent – meaning that in DD case the huge number of clock parameters are just pre-eliminated. The results for all remaining parameters are identical, if no numerical shortcut is done (e.g., ignoring parts of the correlations introduced by the DD approach). The advantage of processing DD observations is the reduced number of parameters, easier detection and potential correction of cycle slips, and direct access to the integer ambiguities without any phase bias parameter. On the other hand, ZD processing offers greater flexibility in network configuration and parameter handling. This is particularly advantageous when modifying the list of stations in the processing, including Low Earth Orbiting satellites (LEOs), or implementing advanced clock models, e.g., for Galileo satellites.
Based on an experimentally developed ZD-based ambiguity resolution method that introduces ambiguity clusters and satellite-wise consistency corrections, the original research prototype was translated into a robust and automated routine processing chain which is suitable for operational use.
The new procedure follows the structure of the established CODE DD strategy but adapts the individual processing steps, e.g. pre-processing, estimation of global parameters, handling of receiver-dependent parameters, and ambiguity resolution. Special emphasis is placed on numerical stability, the reliable handling of real-valued ambiguities, and the introduction of quality-control mechanisms designed for long-term autonomous operation. The resulting procedure enables efficient parallelization and delivers consistent orbit, clock, and ambiguity products. We have investigated the requirements on the station density for all these steps in order to optimize also the processing time. Stations, that are not needed in this context can get pre-processed based on the PPP approach and get added to the final solution only.
Initial results show that the operational ZD processing chain reaches the accuracy and stability of CODE DD-based products while offering greater flexibility for future extensions. The results demonstrate that the ZD-based GNSS processing is sufficiently mature to generate stable global products on a daily basis and therefore represents a promising foundation for next-generation GNSS solutions computed at CODE.
How to cite: Kobel, C., Rodriguez, E., Dach, R., Arnold, D., Brockmann, E., Kalarus, M., Lasser, M., Schär, S., Stebler, P., and Jäggi, A.: Towards Routine zero-difference GNSS Processing at Center for Orbit Determination in Europe (CODE), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4841, https://doi.org/10.5194/egusphere-egu26-4841, 2026.
The correct resolution of integer carrier-phase ambiguities is a key element for improving user precise positioning solutions, especially during the convergence. However, wrongly fixed ambiguities might deteriorate the solution, so posing a potential threat for safety-critical applications. Within the framework of the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method, a Fixed Failure-rate Ratio Test (FFRT) has been proposed to generate ratio-test critical values according to a tolerable failure rate.
For a given failure rate, FFRT thresholds’ computation via Monte Carlo (MC) simulations is generally not computationally efficient. At the same time, with the recent LAMBDA 4.0 toolbox implementation, fitting functions introduced by Hou et al. (2016) were integrated for computing these critical values and therefore controlling the failure rate to prevent unnecessary false alarms. Still, Lookup Tables (LT) represent a conservative approach rather than a close approximation to critical values for a given model strength.
In this contribution we leverage the latest developments in Machine Learning (ML), thus focusing on a Neural Network (NN) function approximation. The latter one considers the components of the ambiguity variance-covariance matrix as input and provides the FFRT critical value for a given failure rate. In this way, it is possible to provide a very accurate approximation (close to MC-based results) with an efficiency comparable to LT approach in use by the latest LAMBDA 4.0 implementation.
For the NN training, several GNSS scenarios are synthetically generated based on actual satellite orbits. Hence, we produce datasets for Precise Point Positioning with Ambiguity Resolution (PPP-AR) user models in an uncombined form, based on a Kalman Filter. It is numerically shown how considering the ‘conditional variances’ as inputs for the NN is sufficient for an approximation of FFRT thresholds, which are otherwise too conservative when using the LT approach developed by Hou et al. (2016). The NN results are therefore assessed based on independent datasets not involved during the training stage.
These three approaches, i.e. MC, NN, LT, are ultimately compared in PPP-AR processing using real data from 30 well-distributed stations from the IGS global network, based on the use of CODE Final products. It is further shown how adoption of fixed critical values for the ratio test, like 2 or 3, often leads to a very conservative ambiguity validation, i.e. returning float solutions when RT is rejected. On the other hand, a properly defined FFRT estimator allows improving user convergence time, as well as enabling more advanced ambiguity validation strategies for PPP-AR, as discussed in this work.
This research has been funded by the ESA’s Navigation Innovation and Support Program (NAVISP) Element 1 programme [https://navisp.esa.int/project/details/307/show].
How to cite: Massarweh, L., Yin, C., Verhagen, S., Liu, X., Odijk, D., Visser, H., and Psychas, D.: A Neural Network approximation of Fixed Failure-rate Ratio Test for PPP Ambiguity Resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18444, https://doi.org/10.5194/egusphere-egu26-18444, 2026.
Real-time estimation of user positioning parameters forms the backbone of high-precision GNSS navigation. The recursive Kalman filter is the most widely adopted estimation method for this task, providing optimal estimates in the minimum variance sense under the assumption that the underlying models are correctly specified. In many practical GNSS applications, however, this assumption may be violated, as measurement models commonly involve state-vector elements that are not naturally linked in time. Imposing incorrect dynamic modeling on such parameters may lead to sub-optimal solutions.
In this contribution, we examine the mechanics of the generalized Kalman filter (Teunissen et al., 2021), which offers a statistically rigorous alternative to the standard Kalman-filter practice of inflating the process-noise variances or assigning arbitrary initial values to states of newly-tracked satellites. Rather than enforcing all state-vector elements to vary in time, the generalized formulation permits only some functions of the state-vector to be linked in time, while others remain unlinked in time. This relaxed dynamic model offers a flexible framework for recursive parameter estimation when limited or insufficient knowledge is available on the temporal behaviour of the involved parameters. Typical applications include purely kinematic precise positioning, network-based satellite clock estimation, kinematic precise orbit determination in low Earth orbit, and GNSS precise positioning during periods with enhanced ionospheric activity.
As any real-time estimation process inevitably requires validation of the underlying models, recursive quality control of the measurement model needs to be executed in parallel with the filter. A direct consequence of the generalized filter is that the classical predicted residuals used in quality control procedures are no longer applicable. It is shown here how these residuals are generalized to predictable functions of the measurements, while practical methods are demonstrated for constructing them in real time for different choices of unlinked-in-time states.
Supported by real-world multi-GNSS simulated kinematic and vehicle-borne datasets, the performance of the generalized Kalman filter using the carrier-phase ambiguity resolution-enabled precise point positioning (PPP-RTK) concept is presented. Next to the positioning performances, the required adaptations to the recursive data quality control procedure, involving both the detection and the identification of mismodeling biases, are illustrated.
Teunissen, P.J.G., Khodabandeh, A. & Psychas, D. A generalized Kalman filter with its precision in recursive form when the stochastic model is misspecified. J Geod 95, 108 (2021). https://doi.org/10.1007/s00190-021-01562-0
How to cite: Psychas, D.: Generalized Kalman filtering applied to real-time high-precision GNSS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21083, https://doi.org/10.5194/egusphere-egu26-21083, 2026.
In recent decades, Precise Point Positioning (PPP) has become a highly effective Global Navigation Satellite System (GNSS) positioning method and promising alternative to well-established relative positioning techniques. PPP is characterised by the use of precise satellite data (such as satellite orbits, clocks, and biases) and the accurate modelling of various error sources. PPP allows us to achieve position accuracies at the centimetre or even millimetre level. However, its widespread use has been limited due to significant convergence times. Among other approaches, innovative PPP models and PPP with integer ambiguity resolution (PPP-AR) have proven to be the most effective in reducing this time.
The decoupled clock model (DCM) provides an elegant way to perform PPP-AR in an uncombined approach for any number of frequencies. The idea is to estimate separate receiver clock errors for code and phase observations, resulting in a receiver code clock error and a receiver phase clock error. Additionally, the unknowns are reparametrized in such a way that the integer property of the phase ambiguities is conserved, with a datum satellite being used to set the phase ambiguities' datum. Unlike other PPP-AR approaches, direct differencing with a reference satellite is not necessary. PPP-AR can therefore be performed in a straightforward manner.
In this contribution, we discuss the DCM and its key characteristics. We present PPP results achieved with the uncombined DCM and GPS, GLONASS, Galileo, and BeiDou observations on three frequencies at thirty-second and one-second intervals. We then evaluate convergence behaviour, coordinate accuracy, ZTD estimation, and ambiguity fixing rates. The PPP investigations were conducted using the open-source software raPPPid. Our findings show that instantaneous PPP convergence to centimetre-level accuracy can be achieved within two to three measurement epochs.
How to cite: Wareyka-Glaner, M. F. and Möller, G.: Instantaneous PPP convergence with a Decoupled Clock Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5001, https://doi.org/10.5194/egusphere-egu26-5001, 2026.
Although tightly coupled Precise Point Positioning/Inertial Navigation Systems (PPP/INS) are capable of decimeter-level accuracy, conventional filtering frameworks suffer from a theoretical disconnect: attitude errors are mapped onto the special orthogonal group SO(3), while position and velocity errors are treated in Euclidean space. This mathematical heterogeneity often induces error accumulation during state propagation. To resolve this, this paper presents a multi-frequency PPP-AR/INS framework based on the Left-Invariant Lie Group. By strictly defining all state errors on the Lie group manifold, estimation consistency is significantly enhanced. Field experiments confirm the superiority of the proposed approach over traditional methods. Specifically, under open-sky conditions, the left-invariant formulation outperforms the right-invariant and conventional method by reducing 3D positioning errors by 3.3% and 9.3%, respectively. In challenging environments with partial signal blockage, the method yields improvements of 4.8% for 2D and 13.1% for 3D. Furthermore, during complete GNSS outages, the enhanced accuracy of the IMU state estimation mitigates drift, lowering 2D and 3D errors by 11.2% and 6.3%, respectively. Notably, these gains are achieved with only a marginal 2.4% increase in computational load, validating the efficiency of the method for real-time applications.
How to cite: Song, H., Geng, T., and Li, Z.: Lie Group model and performance analysis of Triple-Frequency PPP-AR/INS Tightly Coupled Integration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6061, https://doi.org/10.5194/egusphere-egu26-6061, 2026.
Errors and anomalies in real-time State Space Representation (SSR) products can substantially degrade the performance of precise point positioning with ambiguity resolution (PPP-AR). The existing network-based monitoring approaches mainly rely on phase residuals to detect SSR product errors. These methods perform effectively when monitoring stations achieve integer ambiguity resolution, as product errors are then fully expressed in phase residuals. However, for SSR products with ambiguities not fixed, a portion of the product error is absorbed in the float ambiguity estimates while only the remainder manifests in phase residuals. This incomplete error representation prevents reliable assessment, typically leading to the exclusion of these SSR products from service. Such exclusion reduces SSR product availability and can compromise PPP-AR performance in challenging scenarios, where maintaining service continuity is critical. To overcome this issue, this study presents a monitoring and correction framework applicable to SSR products regardless of their ability to support ambiguity resolution. The approach recognizes that product errors in float PPP processing separate into two quantifiable components. The first component is absorbed by float ambiguity parameters and revealed through deviations between estimated ambiguities and their integer values. The second component persists in phase residuals. Extracting and jointly considering both components enables complete error characterization independent of whether ambiguities can be subsequently fixed. The method operates through coordinated processing at multiple monitoring stations. First, the float PPP solutions yield ambiguity deviations and phase residuals for each tracked satellite. These ambiguity deviations exhibit spatial correlation across the monitoring network. Then, wide-area modeling exploits this correlation to estimate systematic and spatially coherent error corrections in the SSR products. The resulting corrections mitigate these error components, after which phase residuals predominantly represent random, uncorrectable errors suitable for anomaly detection and quality evaluation. The experimental validation using real-time SSR products provided by the Centre National d’Études Spatiales (CNES) and wide-area monitoring stations in China demonstrates that the proposed method effectively improves the reliability and availability of SSR products, while significantly enhancing ambiguity resolution robustness and positioning performance in real-time PPP-AR applications. Compared with phase observation residual-based and no-monitoring methods, the proposed method reduces incorrect ambiguity fixing rates by 0.47% and 4.22%, and three-dimensional positioning errors by 71.7% and 82.2%, respectively.
How to cite: Tian, Y., Shu, B., Zheng, Y., González-Casado, G., Gao, Y., Wang, L., and Rovira-Garcia, A.: Monitoring and correction of SSR product errors using PPP float ambiguity deviations and phase residuals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15869, https://doi.org/10.5194/egusphere-egu26-15869, 2026.
Satellite clock offset products are fundamental to high-precision Global Navigation Satellite Systems (GNSS) applications. Clock series derived from L-band Orbit Determination and Time Synchronization (ODTS) method generally exhibit good short-term stability but suffer from pronounced day-boundary discontinuities (DBDs). Furthermore, the orbit modeling errors absorbed into the clock estimates degrade the clock long-term stability. Inter-satellite link (ISL) technology provides an additional source of clock estimated information. Clock offsets derived from geometry-free ISL observables are almost immune to orbit errors and therefore offer better long-term stability. However, the limited ranging precision introduces larger random noise into ISL clocks, leading to the poorer short-term stability. Consequently, combining the above two measurement types is a logical strategy to optimize the overall clock solution. To exploit their complementary characteristics, we apply the Vondrak-Cepek (V-C) filter to combine 31 days of ISL clock offsets with the time derivatives of ODTS clocks provided by Deutsche GeoForschungsZentrum (GFZ). The results demonstrate that the V-C filter effectively suppresses observation noise without distorting the true signals. The combined clock product preserves the continuity of ISL clocks while maintaining the low noise level of the ODTS solution. After quadratic detrending, the combined clock residual is about 0.10 ns, comparable to those of ISL and significantly better than the GFZ value of 0.15 ns. In terms of overlapping Allan deviation, the combined clocks closely follow the GFZ performance at short averaging times and approach or even surpass the ISL stability at long intervals, achieving a balanced compromise between short- and long-term performance. The improvement in stability arises from both the complementary fusion of the two datasets and the smoothing properties of the filter. This study provides a new perspective on GNSS satellite clock combination and a practical method for fully exploiting the strengths of different clock products.
How to cite: Geng, T., Yan, K., Xie, X., and Zhao, Q.: Combination of GNSS satellite clock offsets from L-band ground-tracking and inter-satellite link measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2026, https://doi.org/10.5194/egusphere-egu26-2026, 2026.
Signal distortion bias (SDB) is a systematic pseudorange bias associated with receiver types. Its presence reduces the consistency of GNSS clock and signal bias estimated from inhomogeneous networks, significantly decreasing the ambiguity resolution rate and negatively affecting positioning and timing performance. Current research does not provide a comprehensive understanding of the error characteristics of SDB, and traditional SDB calibration methods exhibit certain limitations.
This paper first identifies the correlation between SDB and factors such as receiver brand, model, as well as the dependencies on firmware versions, antennas, and radomes. In addition, we propose a geometry-free (GF)-aided multi-GNSS all-frequency SDB calibration method and strategy. The GF assistance addresses the ±0.5 cycle limitation associated with wide-lane (WL) ambiguity rounding, further improving the precision of SDB calibration. Based on this method and MGEX data, we calibrated and analyzed the SDB for all-frequency signals of GPS, Galileo, and BDS-3. Ultimately, we provide SDB corrections for all satellites per receiver group in SINEX BIAS format.
We further investigated the impact of SDB on satellite clocks, code biases, phase biases, and PPP-AR both theoretically and experimentally. Results show that SDB correction significantly enhance the consistency of satellite clock and bias estimates across networks, with BDS-3 improving by over 90% and wide-lane ambiguity fixing rates for different receivers increasing by up to 20% at most. Moreover, an analysis of one-month data from 132 MGEX stations with hourly reset indicates that SDB correction reduces the multi-GNSS kinematic PPP convergence times for Septentrio, Leica, Javad, and Trimble receivers by an average of 0.43, 2.74, 2.63, and 4.28 epochs, respectively, with corresponding PPP-AR convergence improvements of 1.9%, 8.5%, 14.7%, and 17.7%. The average convergence performance across stations with different receiver types improved by 2.4%, 12.1%, 31.3%, and 25.2%, with maximum improvements of up to 70.8%. These analyses fully demonstrate the necessity of SDB modeling and correction, and it is recommended that the IGS Analysis Center adopt it.
How to cite: Xu, S., Guo, J., Li, J., and Zhao, Q.: Multi-GNSS all-frequency SDB calibration and its impact on high-precision products and positioning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15669, https://doi.org/10.5194/egusphere-egu26-15669, 2026.
All geodetic space techniques used today are based on measuring the propagation time of electromagnetic waves between a transmitter and a receiver. The atmosphere is one of the most limiting factors for the achievable accuracy of the observation techniques. The wet part of the troposphere, particularly water vapour, has a strong and highly variable influence on signals in the microwave range, as is the case with GNSS. By using current models, this high variability makes it difficult to sufficiently correct the influence of the troposphere on the signal propagation time. Neither weather models nor estimation of parameters can accurately capture the high tropospheric variability. An alternative method for determining the wet propagation delay is a water vapour radiometer.
With the HATPRO-G5, the Geodetic Observatory Wettzell has a modern radiometer that is capable of measuring the wet part of the troposphere above the station not only at the zenith but also for various azimuth-elevation combinations. This allows the wet delay to be recorded as a function of azimuth, elevation, and time. The resulting data set will first be analyzed and then used in combination with GNSS observations. To make this data available for GNSS evaluation, the water vapour radiometer data must be smoothed and interpolated both spatially and temporally. To assess the impact of radiometer-based tropospheric correction, the estimated station height component is compared with a standard GNSS processing and with the height component of a station coordinate product. We performed a PPP to estimate the station coordinates. However, the results do not show a clear picture. On the one hand, the estimation of the station height component using radiometer-based correction appears to deliver better results than the classic approach for certain time periods. On the other hand, however, these improvements cannot be reproduced for the entire time period of the data set. The data set still exhibits systematic errors whose origin has not yet been clarified, which in turn negatively affect the accuracy of the height component estimate. Possible reasons for this could include rapid weather changes, rain events or also electronic-specific systematics. Nevertheless, radiometer-based corrections have the potential to positively influence the accuracy of parameter estimation in GNSS processing.
How to cite: Vollmair, P., Schlicht, A., Klügel, T., and Hugentobler, U.: Integration of radiometer data to improve tropospheric correction in GNSS-PPP processing., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9479, https://doi.org/10.5194/egusphere-egu26-9479, 2026.
Accurate modeling of the horizontal and vertical variation of the tropospheric horizontal gradient is crucial for correcting space-geodetic tropospheric delays and retrieving atmospheric water vapor. To date, the horizontal variation in the gradient has been well represented by the operational grid-wise products, such as grid-wise GRAD. However, for vertical variation, the relative methods and models are unavailable, which limits the accuracy of the grid-wise products by ignoring the vertical variation from grid height to target height. In this contribution, we analyze the variation characteristics of the asymmetric delay and the horizontal gradient using ERA5 data and the ray-tracing technique, and confirm their significant reductions with height. Then, we introduce a new height-correction method that adjusts for vertical variation in the horizontal gradient based on its characteristic behavior, and the results demonstrate that accounting for height variation can significantly reduce residuals in horizontal gradient modeling. Finally, we developed a global model using this method and validated it against the grid-wise GRAD products, with the site-wise GRAD products as the reference. The results demonstrate that the height-correction model can significantly improve the accuracy of grid-wise GRAD at high-altitude locations, making it suitable for high-altitude stations and unmanned aerial vehicle applications.
How to cite: Zhou, Y., Lou, Y., Zhang, W., and Li, X.: Vertical variation modeling of the tropospheric horizontal gradient, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8623, https://doi.org/10.5194/egusphere-egu26-8623, 2026.
Smartphones have evolved into ubiquitous platforms equipped with a sophisticated array of sensors, including cameras, IMUs, GNSS receivers, LiDAR, and magnetometers. These sensors enable a wide range of applications, from navigation and mapping to virtual reality and accessibility tools for the visually impaired. However, despite this hardware potential, the development of robust multi-sensor fusion algorithms remains constrained by data limitations.
While existing public datasets—such as those from the Google Smartphone Decimeter Challenge (GSDC)—have advanced high-accuracy positioning by integrating GNSS and IMU data, they often overlook complementary sensors like cameras and magnetometers. Furthermore, current data collection tools (e.g., GNSSLogger, Sensor Logger) often lack the capability to log raw GNSS observations and visual data simultaneously with precise time synchronization. This gap hinders the application of emerging machine learning techniques that require diverse, synchronized input streams.
In this contribution, we introduce a custom Android application capable of collecting time-synchronized data from multiple sensors, including IMU, camera, GNSS, and magnetometer. We evaluate the time-synchronization capabilities of popular smartphone models, including the Google Pixel series, Xiaomi, Samsung, and OnePlus. Using this application, we compiled a comprehensive dataset in Calgary, Alberta, Canada, capturing diverse environments such as urban canyons, highways, parks, and farmland under varying weather conditions. The data includes both vehicle-mounted and handheld kinematic scenarios. Finally, to demonstrate the utility of the dataset, we establish a performance benchmark using conventional open-source software, such as VINS. This work provides the research community with a holistic benchmark dataset to advance multi-sensor fusion algorithms for smartphones.
How to cite: Yang, H. and Nie, S.: Multi-Sensor Smartphone Mapping and Positioning: App, Dataset, and Benchmark, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22106, https://doi.org/10.5194/egusphere-egu26-22106, 2026.
The positioning accuracy of smartphone-based Global Navigation Satellite Systems (GNSS) is significantly degraded by ionospheric delay. Current correction methods primarily rely on broadcast ionospheric models, which offer limited precision. High-precision real-time ionospheric grid products, while more accurate, require a stable internet connection and incur additional costs, posing a significant constraint for mobile applications in network-free environments. To address this challenge, we propose ST-VIT-NET, a novel deep learning model based on the Vision Transformer (ViT) architecture for real-time ionospheric prediction and correction. ST-VIT-NET learns the deviation between the Klobuchar broadcast model and the high-precision final ionospheric grid product IGS-GIM, thereby enabling high-accuracy ionospheric correction for broadcast ephemeris-based models.
Experimental results demonstrate that, on a global scale, the ST-VIT-NET model achieved an average Root Mean Square (RMS) error of 4.37 TECU in predicting the Vertical Total Electron Content (VTEC) over a 131-day period from day of year 161 to 292 in 2025. This represents reductions of 64.44% and 9.34% compared to the Klobuchar model (12.29 TECU) and the IGS real-time GIM model (4.82 TECU), respectively, indicating strong temporal and spatial generalizability. In static positioning tests, Standard Point Positioning (SPP) using the ST-VIT-NET model with a Huawei P40 smartphone yielded horizontal and vertical RMS positioning errors of 1.35 m and 2.18 m. These values are 52.42% and 62.77% lower than those obtained using the Klobuchar model (2.85 m horizontal, 5.86 m vertical), and 25.42% and 13.71% lower than those using the IGS real-time GIM model (1.82 m horizontal, 2.53 m vertical). In kinematic vehicle tests, SPP using the ST-VIT-NET model with a Huawei Mate40 smartphone resulted in horizontal and vertical RMS errors of 2.37 m and 3.81 m. This corresponds to reductions of 59.62% and 62.51% compared to the Klobuchar model (5.87 m horizontal, 10.16 m vertical), and 13.41% and 33.01% compared to the IGS real-time GIM model (2.74 m horizontal, 5.69 m vertical).
Collectively, the findings confirm two key contributions of the proposed model. First, ST-VIT-NET demonstrates strong temporal and spatial generalizability, as evidenced by its sustained high-precision VTEC prediction capability over an extended 131-day period across diverse regions. Second, it provides a viable and self-contained solution for achieving real-time high-precision GNSS positioning on smartphones in network-free scenarios, as it delivers accurate ionospheric corrections using only onboard GNSS observations without any external data dependency.
How to cite: Li, G., Geng, J., Lu, J., and Sieradzki, R.: Enhancing Smartphone GNSS Positioning through Deep Learning-Based Ionospheric Prediction and Correction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11417, https://doi.org/10.5194/egusphere-egu26-11417, 2026.
The use of low-cost Global Navigation Satellite System (GNSS) receivers has emerged as a promising strategy to densify existing geodetic networks and to enhance the spatial resolution of atmospheric monitoring at regional scales. This study presents the deployment and long-term operation of a low-cost GNSS network consisting of five stations installed across the province of Jaén (southern Spain), aimed at complementing the regional permanent GNSS network for the monitoring of atmospheric, climatological and environmental processes. In addition, a sixth low-cost receiver was placed at San Fernando along with the IGS reference station SFER and an operational AEMET meteorological station, enabling direct intercomparisons with high-grade geodetic receiver using meteorological observations to generate atmospheric parameters.
The network has been operating continuously since November 2022. GNSS data were processed using the PRIDE PPP-AR software package up to July 2025, providing precise estimates of the tropospheric total delay. The wet component of the tropospheric delay, combined with in situ surface meteorological measurements, was used to derive precipitable water vapour (PWV) time series for each station. These PWV estimates were systematically compared with independent data sources, including ERA5 reanalysis products and satellite-based post-processed solutions, in order to assess the consistency, stability and accuracy of the low-cost GNSS-derived atmospheric parameters.
The results highlight the capability of low-cost GNSS receivers to capture meaningful atmospheric variability and demonstrate the added value of network densification in regions with different characteristics and sparse permanent instrumentation. The observed differences with respect to coarser-resolution datasets underline the potential of such networks for improving the monitoring and early detection of adverse meteorological phenomena. This study supports the feasibility of using low-cost GNSS technology as a reliable and cost-effective complement to existing geodetic and meteorological observing systems.
How to cite: Rodríguez Collantes, D., de Lacy Pérez de los Cobos, M. C., Garrido Carretero, M. S., and Retegui Schiettekatte, L.: Assessing the Potential of Low-Cost GNSS Network Densification for Regional Atmospheric Monitoring in Southern Spain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20095, https://doi.org/10.5194/egusphere-egu26-20095, 2026.
Global Navigation Satellite Systems (GNSS) coordinate time series is utilised in geodesy for different purposes, e.g., Earth deformation monitoring. Different signals, such as annual, semi-annual, and draconitic year signals, as well as noise, are superimposed on the GNSS coordinate time series. In addition, a velocity signal caused by plate tectonic movements is present in the horizontal components, and uplift or subsidence exists in the vertical component of the GNSS coordinate time series. We require performing signal separation algorithms to decompose the time series into meaningful signals. In this study, we utilise Monte Carlo singular spectrum analysis (MC-SSA) for signal separation and hierarchical clustering for grouping the modes derived from SSA. We also use least-squares variance component estimation (LS-VCE) and fast LS-VCE for noise determination throughout the whole data processing. Finally, the velocity is determined using least-squares regression after removing the periodic signals and utilising the covariance matrix determined by LS-VCE and fast LS-VCE as a stochastic model. The results are presented in both spatial and temporal domains, which can be used to detect, for example, the phase shift in both domains. The final velocity field and the uncertainty for the up component are also extracted for the GNSS stations in Europe.
How to cite: Karimi, H. and Hugentobler, U.: GNSS coordinate time series analysis and signal separation applied to Earth deformation monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20523, https://doi.org/10.5194/egusphere-egu26-20523, 2026.
The Global Navigation Satellite Systems (GNSSs) have the invaluable ability to monitor the crustal deformation either for geodesy (determination of the shape of the Earth) or for geophysics (interpretation of geodynamical processes). However, these systems have some limitations. Among the others, the systematic errors and unmodelled effects defined and observed as a common mode error (CME) have to be mentioned. Isolation of CME from displacements seems to be crucial for obtaining reliable velocities and their uncertainties. In this research we use a set of European GPS-derived vertical displacements recorded at 4443 permanent stations provided by the Nevada Geodetic Laboratory (NGL) and, in the first step, we compare them with displacements predicted by non-tidal atmospheric (NTAL), hydrospheric (HYDL), oceanic (NTOL), and barystatic sea level (SLEL) loading models provided by the GFZ Helmholtz Centre for Geosciences to obtain a consistent picture of GPS sensitivity to loadings over Europe. This part of the study allowed us to confirm a very high correlation but varied depending on the region of Europe. Then, we divided GPS stations regionally upon the results of noise analysis and the common mode error was determined using the probabilistic Principal Component Analysis (pPCA) method. We note a significant correlation between the NTAL model and the CME values, which indicates that in Europe, most of the CME is driven by the unmodeled atmospheric effect with some regional anomalies. Finally, we provide a discussion on the differences in the values of velocities of GPS permanent stations together with their uncertainties after removing the CME values.
How to cite: Bogusz, J. and Klos, A.: Exploring the common mode error: case study of Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1667, https://doi.org/10.5194/egusphere-egu26-1667, 2026.
Seasonal variations in GNSS coordinate time series are largely driven by environmental mass loadings, which involve not only instantaneous elastic deformation but also time-dependent poroelastic and hydrological responses. Assuming an instantaneous Earth response may bias long-term deformation estimates and affect reference frame stability. We select 4,711 global vertical GNSS coordinate time series spanning at least a decade with > 95% data completeness, together with their corresponding environmental loading deformation time series, both provided by the Nevada Geodetic Laboratory. To address the combined effects of multiple environmental loads, we first identify the dominant loading component at each station — defined as the one contributing more than 75% of the total modeled seasonal deformation amplitude. Then we extract annual signals from both GNSS and the dominant loading deformation time series using Singular Spectrum Analysis and estimate the phase lags between them through cross-correlation algorithm. This approach minimizes interference from secondary loading sources and provides a clean estimate of the time lag associated with the primary driving process. Globally, hydrological loading induces phase delays with significant spatial variability (standard deviation: 41 days). These phase lags exhibit systematic spatial patterns, possibly reflecting diverse hydrological processes across regions: negative delays (GNSS lagging load by ~ 28 days) in the mountainous western United States are possibly associated with unmodeled deep subsurface water retention; widespread positive delays (GNSS leading load by ~ 13 days) in the U.S. Corn Belt and Europe suggest rapid water removal due to human activity; and anti-phase anomalies (difference of > 150 days) in confined aquifers may reflect poroelastic responses. Applying the phase-lag correction by subtracting the time-shifted annual loading signals from GNSS observations improves the consistency between them at ~ 70% of the stations compared to the standard instantaneous correction, with a median reduction in annual signal power increasing from 40.4% to 62.1% (69.2% to 85.2% in regions with strong hydrological loading like the Amazon and the mountainous western U.S.) and a concurrent increase in the median weighted root-mean-square (WRMS) reduction from 2.7% to 4.0% (7.2% to 9.7% in the aforementioned regions). It also mitigates potential bias in site-specific velocity estimates (up to 0.04 mm/yr). Our results demonstrate that accounting for phase lags between GNSS observations and loading models is important for refining loading corrections and thereby enhancing the stability of geodetic reference frames.
How to cite: Li, B., Zhou, X., Yang, Y., and Chen, Q.: Global Phase Lags between GNSS and Modeled Hydrological Loading: Implications for Hydrogeophysical Responses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7504, https://doi.org/10.5194/egusphere-egu26-7504, 2026.
The Greenland GNSS Network (GNET) consists of 71 geodetic-grade Global Navigation Satellite Systems
(GNSS) stations mounted directly in stable bedrock along the perimeter of the Greenland Ice Sheet
(GRLS). The first continuously running GNSS (cGNSS) station pre-GNET, was set up in 1995 and has
been operating continuously since then. During the fourth International Polar Year (IPY, 2007–2008),
GNET was established with the addition of 49 remote stations, with additional sites added during the
following years. Over time, the installations have undergone various updates, which have helped to
stabilize and improve observations from the network. Early GPS-only receivers have gradually been
replaced by multi-constellation systems, improving positioning precision. The continuous updates have
resulted in a yearly mean of received observations to be above 95% since around 2020 across the network.
Operating cGNSS stations in the remote high Arctic is challenging and can give rise to downtime for
stations in the network. In this project, we aim to publish the most comprehensive, fully processed
positional time-series from GNET up to date, provided as geodetic coordinates and in a local East,
North, Up (ENU) frame, together with metadata documenting station history and development. The
processing is performed using the Precise Point Positioning (PPP) methodology implemented in the
GipsyX 2.5 software [Bertiger et al., 2020].
To evaluate the quality and performance of the processed time series, two analyses are performed. First,
a long-term stability analysis is carried out by fitting and removing a seasonal trajectory model from each
ENU component individually. The residuals are then used to estimate power-law noise characteristics,
derived from Lomb–Scargle periodograms. The analysis shows that all stations in the network can be
expected to operate with a noise profile in the flicker-noise region, −1 < κ < 1. [Goudarzi et al., 2015]
Second, we compare our processed positional time series with two previously published products from
other processing centers: the Jet Propulsion Laboratory (JPL) [NASA Jet Propulsion Laboratory, 2018]
and the Nevada Geodetic Laboratory (NGL) [Blewitt et al., 2018]. The comparison, based on 39 of 71
stations, uses inter-metric correlation analysis of trajectory model parameters fitted to individual time
series. The results show good agreement among the three products in the vertical direction but poor
correlation in horizontal displacements. The JPL and NGL products exhibit a small, non-zero seasonal
signal in the horizontal components, which is not expected [White et al., 2022; Bian et al., 2023; Materna
et al., 2021]. The amplitudes of these signals, however, are very small, suggesting that these signals likely
originate from specific modeling and processing choices during the PPP processing. Consequently, while
the vertical seasonal signals can be interpreted confidently, horizontal seasonal amplitudes and phases
should be treated with caution when using time-series from NGL or JPL compared to the product we
publish.
Overall, the results highlight the importance of processing strategy, noise characterization, and validation
for high-precision GNSS time series in geoscience applications.
How to cite: Solgaard, C., Winther-Dahl, M., Nylen, T. H., Madsen, F. B., Bjerregaard, O., Longfors Berg, D., Knudsen, P., and Abbas Khan, S.: The Greenland GNSS Network (GNET): Long-Term Stability and Validation of Geodetic-Grade GNSS Measurements of Greenland’s 3D Bedrock Displacement from 1995–2025 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12342, https://doi.org/10.5194/egusphere-egu26-12342, 2026.
How to cite: Qu, X. and Ding, X.: Real-time Rotation Monitoring of Offshore Structures based on GNSS and Accelerometer Data Fusion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2231, https://doi.org/10.5194/egusphere-egu26-2231, 2026.
Posters on site: Wed, 6 May, 10:45–12:30 | Hall X1
Strong-motion acceleration records are crucial for seismic research and vibration analysis. Still, baseline offsets often introduce drift in displacement integration estimates, compromising the accuracy of the coseismic displacement retrieval. By providing precise ground deformation signals, the high-rate Global Navigation Satellite System (GNSS) offers ideal baseline correction constraints. In this paper, we propose a baseline correction method based on acceleration smoothness priors and co-located high-rate GNSS static displacement constraints. First, accelerations are processed using the smoothness priors method (SPM). This method separates steady-state acceleration signals from potential non-periodic noise trends through regularization. Second, static displacements from co-located high-rate GNSS stations are applied as external constraints to refine a step-fitting function. The optimal baseline correction time parameters are iteratively determined through a grid search method. Finally, the displacement time series is then fitted with this constrained step-fitting function to achieve baseline deviation correction of acceleration records. A shake table experiment and two seismic events validated the proposed baseline correction method. In the shake table experiment, the corrected displacement time series returned to the zero line, preserving long-period and permanent displacement information, with a root mean square (RMS) of 0.585 cm and a correlation coefficient (CC) of 0.964. For the 2023 Turkey earthquake doublet, the corrected strong-motion displacements showed good agreement with GNSS data, achieving an average RMS of 2.720 cm and a CC of 0.799. For the 2021 Maduo Mw 7.4 event, the method yielded an RMS of 0.585 cm and a CC of 0.964, with an average RMS of 1.157 cm and a CC of 0.592 in all directions. This demonstrates its potential as a key technique for accurately retrieving source parameters and finite fault slip inversion from strong-motion accelerometer data.
How to cite: Zhang, Y., Li, Y., and Shan, X.: Strong motion baseline correction based on acceleration smoothness priors and co-located GNSS static displacement constraints, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1482, https://doi.org/10.5194/egusphere-egu26-1482, 2026.
GNSS precise point positioning (PPP) requires precise satellite orbit, clock and code/phase bias products. The International GNSS Service (IGS) has been operationally providing such high-precision valuables in support of science and society. Since the IGS encompasses various analysis centers (ACs), it is usual practice to compare these solutions to provide feedback to the ACs and also to combine them to generate the official IGS products with higher stability, reliability, completeness, and robustness. Under the current IGS framework, the AC Coordinator (ACC, acc.igs.org) manages orbit and clock combinations, while the Ionosphere Committee (IC, igs.org/wg/ionosphere) and Reference Frame Committee (RFC, igs.org/wg/reference-frame) are responsible for ionosphere products and station coordinates. Meanwhile, the newly established Wuhan Combination Center (WCC, igs.org/wg/wcc) is acting as an experimental alternative to augment the legacy combination procedures. Usually, a “summary” file containing the combined statistical results is generated to facilitate contributions from ACs. However, such a summary file format shows a few limitations, e.g., failure in complying with a dedicated format standard, no statistics for the combined code/phase bias products and insufficient quality evaluation indices, such as anomalous satellites, outlier clocks, and so on. Consequently, these limitations hinder automated parsing and diminish human interpretability of the summary results. During the 2025 Governing Board meeting in Rimini, the IGS formed a task force led by WCC in collaboration with ACC and the Infrastructure Committee, to define a format standard to address the limitations of the traditional summary file. Specifically, the primary objectives of the task force are threefold: first, to establish a format that enables ACs to easily inspect their products artifacts; second, to establish a format that allows PPP and other users to easily exclude outlier products; and third, to develop auxiliary scripts for plotting the combination statistics. The proposed format is expected to support ACs in troubleshooting and verification of their products, along with downstream users for both network and PPP processing, e.g., by enabling consistent quality screening and exclusion of outlier products.
How to cite: Geng, J., Wen, Q., Wang, B., and Zhang, Y. and the The IGS Task Force to Standardize GNSS Product Combination Statistics: The IGS task force for a standardized format of GNSS satellite product combination statistics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2264, https://doi.org/10.5194/egusphere-egu26-2264, 2026.
Precise Point Positioning with Real-Time Kinematic (PPP-RTK) has emerged as a key technique for achieving rapid, centimeter-level positioning by enabling user-side ambiguity resolution through precise satellite products and regionally derived atmospheric corrections. In low-latitude regions such as Thailand, however, pronounced spatial and temporal variability of precipitable water vapor—driven by monsoon dynamics and severe convective weather—remains a major limiting factor for fast convergence and robust positioning performance. This study aims to optimize the interpolation of zenith wet delay (ZWD) and its associated horizontal gradients derived from a regional GNSS reference network for PPP-RTK applications using the GFZ in-house RTPPP software. The proposed strategy consists of two sequential interpolation stages. First, tropospheric parameters are interpolated from GNSS reference stations onto a predefined regional grid. Second, the gridded corrections are interpolated to the rover location. Ordinary Kriging and Universal Kriging are investigated for the reference-station-to-grid interpolation, while bilinear and nearest-neighbor methods are applied for the grid-to-rover interpolation. The performance of the proposed approaches is systematically evaluated with respect to ZWD and horizontal gradient estimates at selected GNSS reference stations, derived independently using a standard PPP solution. In addition, predefined grid sizes of 1 km, 5 km, and 10 km are assessed to determine the optimal grid resolution for ZWD and horizontal gradient interpolation.
How to cite: Kitpracha, C., He, S., Brack, A., and Satirapod, C.: An optimization of the tropospheric correction interpolation method for PPP-RTK technique in Thailand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4736, https://doi.org/10.5194/egusphere-egu26-4736, 2026.
GNSS measurements are recognized as a key technique for ionosphere monitoring. Such a goal is typically realized with datasets from global or regional networks of permanent stations, which provide, e.g., vertical total electron content maps. Despite the unquestioned role of such an approach, it still suffers from an irregular distribution of on-ground monitoring sites, limiting the precision of GNSS-based ionospheric products. A solution addressing this issue is to adopt dual-frequency measurements from low-cost devices, particularly those provided by GNSS chipsets embedded in modern smartphones. These ubiquitous devices can, theoretically, lead to the extreme densification of ionospheric information; however, their widespread use must be preceded by a detailed analysis of data properties and quality.
This still-open issue motivated us to investigate the applicability of ionosphere monitoring using a geometry-free linear combination (GF LC) series built of smartphone-acquired GNSS phase data. In the experiment, we used two smartphones: Google Pixel 7 and Xiaomi 15T Pro, which provide multi-system dual-frequency measurements. The smartphone results were validated against those provided by a high-grade receiver - Trimble Alloy. The dataset comprised GPS, Galileo, and BDS observations collected during three 8-hour sessions. We analysed the completeness and quality of the data, including the noise level, the number of cycle slips, and the consistency and accuracy of smartphone GF LC time series in comparison to the benchmark values. While the analysis confirmed the applicability of smartphone measurements for ionospheric studies, it also revealed the poorer quality of all analysed characteristics. Furthermore, we observe a substantial performance discrepancy between the tested mobile devices, which may pose a problem for their combined utilization.
How to cite: Sieradzki, R., Paziewski, J., Szczepanik, H., Geng, J., and Li, G.: On the feasibility of ionospheric sounding with modern dual-frequency smartphones, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6481, https://doi.org/10.5194/egusphere-egu26-6481, 2026.
The thermoelastic deformation of GNSS monuments and foundations is a significant contributor to the vertical nonlinear variations observed in the GNSS coordinate time series. A rigorous thermoelastic effect model, which takes into account the diversity of monument depths and foundation types of the reference stations, was proposed in this paper to quantify the influence of thermal expansion on the vertical displacement using 409 GNSS reference stations in Mainland China. The periodic characteristics of the GNSS vertical time series, GREL time series, and thermal expansion effect show a higher consistency between the GREL time series and the thermal expansion effects. The annual amplitude of thermal expansion for GNSS reference stations across Mainland China ranges from 0.2 mm to 1.9 mm, increasing with latitude, with a characteristic distribution of lower values in the south and higher in the north. After applying thermal expansion corrections, 79.2% of the reference stations across Mainland China exhibit a decreasing trend in amplitude, with an average reduction of 0.4 mm. Regional variability in the correction effects is significant: the largest corrections occur in Central China, followed by Northwest China and East China, while corrections in Southwest China, South China, and North China are comparatively smaller, and the corrections in Northeast China are negative. The thermal expansion correction demonstrates similar effectiveness across different types of foundational reference stations. A reduction in amplitude was observed in 78.5% of bedrock monument stations and 80.2% of soil monument stations, with a difference of 1.7% between the two. The average reduction in amplitude variation for different types was the same at 0.4 mm, indicating a comparable effect. Notably, the correction effect varies significantly based on the burial depth of the GNSS monuments, with the largest correction observed for the 5.5 m soil monument stations, followed by 2 m bedrock monument stations, 18 m soil monument stations, and 7.5 m soil monument stations. The correction effect for 8.5 m soil monument stations is negative.
How to cite: Wu, J., Li, Z., Wang, X., Zhang, Y., and Liu, C.: The Impact of Thermal Expansion on Nonlinear Vertical Variations of GNSS Reference Stations in Mainland China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8958, https://doi.org/10.5194/egusphere-egu26-8958, 2026.
Dual-frequency ionosphere-free (IF) combinations, commonly used in real-time low Earth orbit (LEO) kinematic orbit determination, effectively remove ionospheric delay errors but significantly amplify noise in code pseudorange observations, thereby limiting orbit accuracy. To address this limitation and enhance real-time orbit precision, this study investigates a code–carrier smoothing approach integrated into a standard point positioning (SPP)-based kinematic orbit determination (OD) framework.
The Sentinel‑6A satellite’s onboard GNSS code pseudorange observations were processed in real-time mode using least squares estimation (LSE) to estimate the satellite’s position, velocity, and clock offset. Ionospheric effects were mitigated by applying IF combinations derived from GPS (L1/L2) and Galileo (E1/E5) dual-frequency signals. To suppress short-term code noise, a Hatch filter-based code–carrier smoothing technique was implemented, in which noisy code pseudorange measurements were combined with precise carrier-phase measurements to produce stabilized pseudorange observables. A smoothing window constant of 16 epochs was adopted to enable recursive real-time processing.
GNSS observation data in RINEX format and reference SP3 orbit products for the Sentinel‑6A satellite were obtained from the Crustal Dynamics Data Information System (CDDIS) and the European Space Agency (ESA) archives. The dataset consisted of 10-second sampling over a 24-hour period starting at 00:00 UTC on April 20, 2025. Broadcast ephemerides of GPS and Galileo satellites were used for real-time orbit derivation.
The kinematic orbit estimates were evaluated at 10-second intervals using the SP3 orbits as reference truth. Without smoothing, the root-mean-squared errors (RMSEs) were 59.5 cm (radial), 27.9 cm (along-track), and 22.9 cm (cross-track), yielding a 3D RMSE of 69.6 cm. With the Hatch filter applied, the corresponding RMSEs improved to 50.0 cm, 23.1 cm, and 18.1 cm, resulting in a 3D RMSE of 58.0 cm. These results represent improvement rates of 16.0%, 17.2%, 21.0%, and 16.7% in the radial, along-track, cross-track, and 3D directions, respectively.
The findings confirm that Hatch filter-based code–carrier smoothing effectively reduces pseudorange noise and improves the precision of real-time kinematic orbit determination for LEO satellites.
How to cite: Lee, H.-S., Choi, H.-Y., and Park, K.-D.: Real-Time LEO Satellite Kinematic Orbit Determination Using Code-Carrier Smoothing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6051, https://doi.org/10.5194/egusphere-egu26-6051, 2026.
High-quality GNSS receivers and strong-motion accelerometers provide reliable and conventional data, now commonly used in early warning systems for geohazards, seismology, and civil engineering applications. Although their capability to deliver precise measurements has been established, their overall monitoring effectiveness can be constrained by the insufficient spatial coverage and densification of these sensors. Conversely, we are now in an era where mass-produced, affordable GNSS and MEMS sensors are widely available, and recent research indicates that they can offer precise GNSS and accelerometer observations. Particular attention is given to sensors embedded in modern smartphones. These widely used, but unprofessional sensors could potentially serve as detectors and providers of rapid, spatially dense information on dynamic movements. In this study, we explore and validate the applicability of precise dynamic displacement detection using GNSS observations and accelerometer data from sensors embedded in selected recent smartphones, such as Xiaomi 14. The validation was performed by means of retrieving artificial vibrations and dynamic motions, which were induced by the Quanser I-40 shake table. With the proposed combined GNSS and accelerometer solution based on smartphone data, we demonstrate the feasibility of precisely detecting sub-centimeter dynamic displacements. The results of the proposed approach based on smartphone data show that smartphones may soon be considered an auxiliary instrument in seismic and structural health monitoring applications.
How to cite: Paziewski, J., Sieradzki, R., and Szczepanik, H.: On the path to dynamic displacement detection with smartphone GNSS and accelerometer data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6652, https://doi.org/10.5194/egusphere-egu26-6652, 2026.
Multipath noise, a site-specific error influenced by the surrounding environment of Global Navigation Satellite System (GNSS) antennas, continues to be a significant challenge in kinematic GNSS positioning, particularly for detecting subtle crustal deformations over hours to days. While the sidereal filter is widely used for its simplicity, it is not always well-suited for multi-GNSS data. The Multipath Hemispherical Map (MHM) method offers better performance and multi-GNSS compatibility. Still, it is often implemented in a software-dependent manner, and isolating multipath from other errors, such as tropospheric delays, is difficult. To overcome these issues, we propose the Multi-Site Stacked MHM (MSS-MHM) method, which builds a hemispherical map by stacking carrier-phase residuals from multiple short-baseline relative positioning solutions. In this setup, residuals that substantially reduce common-mode tropospheric, ionospheric, and clock errors are mapped onto a satellite azimuth-elevation grid. This map is then used to correct the original Receiver Independent Exchange Format (RINEX) observation file, thereby enabling use with a wide range of software. Applying MSS-MHM to 30-second long-baseline kinematic analyses with multi-GNSS data showed that increasing both the number of baselines and stacking days significantly reduced coordinate time series noise. Power spectral density analysis indicated that noise reduction was most effective for periods longer than ~1,000 seconds. Moreover, using the corrected RINEX file across four different positioning software packages (Double-Difference and Precise Point Positioning strategies) improved coordinate stability in our tests. These results highlight MSS-MHM as a software-agnostic, observation-level correction framework for multipath mitigation, applicable across the strategies and software evaluated here.
Acknowledgments: The SoftBank's GNSS observation data used in this study was provided by SoftBank Corp. and ALES Corp. through the framework of the "Consortium to utilize the SoftBank original reference sites for Earth and Space Science."
How to cite: Ito, Y. and Ohta, Y.: Observation-Domain Multipath Mitigation in Global Navigation Satellite System Positioning Using Multi-Baseline Stacked Carrier-Phase Residuals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6782, https://doi.org/10.5194/egusphere-egu26-6782, 2026.
Precise Point Positioning (PPP) is widely used in high-precision GNSS applications, and its performance depends primarily on the orbit and clock products provided by the International GNSS Service (IGS). This study evaluates the impact of using products derived from different IGS Analysis Centers (ACs) on PPP solution quality, using data collected during a GNSS campaign conducted to study an active fault in Oran, Algeria. Data from multiple GNSS stations, located in both open-sky and challenging/constrained environments, were processed using static and kinematic PPP modes with various IGS ACs products. As long-duration observations showed no significant differences, only 30-minute sessions were processed to provide a clear evaluation of solution quality. Position uncertainties, carrier-phase residuals, and kinematic position repeatabilities were analyzed to assess the quality of the solutions. To ensure a fair comparison between the ACs’ products, the analysis was limited to products with the same number of observed satellites and a consistent 30-second clock rate. Furthermore, to include the maximum number of ACs, both OPS and MGEX products were considered, and only GPS and GLONASS satellites were used, as these are provided by the majority of IGS ACs.
Our analysis of epoch-wise phase residuals across multiple IGS ACs, indicates that the residual behavior is remarkably consistent across stations. Exceptions occur at more challenging stations, where some ACs’ solutions, such as GRG, exhibit slightly lower residuals quality. Similarly, for solution repeatability, most ACs products provide comparable results, with GRG again performing slightly worse, particularly in the vertical component showing an RMS repeatability of approximately 30 mm compared to 23 mm at the challenging station AGF21. In terms of positional uncertainties, all solutions demonstrate good horizontal and vertical precision, with values ranging from 1 to 4 mm. Among the ACs, COD (MGX), GFZ, and GRG show marginally better horizontal performance, while JGX performs slightly worse in both components. Vertical uncertainties benefit as well, with improvements of about 4 to 6 mm observed when using COD (MGX), GFZ, and GRG products, especially at the more challenging stations.
How to cite: Bouaoula, W. and Hasni, K.: Impact of using products from different IGS analysis centers on PPP solution quality : A GNSS campaign in Oran, Algeria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7171, https://doi.org/10.5194/egusphere-egu26-7171, 2026.
Railway reference stations are characterized by long span, strip distribution, cross-regional service, weak graphical structure, and requiring high monitoring precision. In view of this, the traditional modeling approaches for reference uniformity have become no longer quite applicable. Currently, coordinates reference maintenance is conducted separately in each area, so that there exist differences among various reference stations in respect of data processing software, mathematical physical model, solution strategy, adjustment method, and revision cycle. This causes systematic deviation in satellite navigation and positioning service reference between different areas. To ensure the uniformity and timeliness of high-precision location-based service reference of national railways, this study is intended to simulate cross-region, strip, isoheight-shape and Y-shape layout modes based on the actual situation of railway observation network, and implement selection of reference station control points, networking solution scheme design and result analysis considering the reference uniformity solution requirements of different layout modes to propose an inclusive coordinates reference uniformity solution scheme applicable for various strip layout modes and provide a practicable reference for the coordinates reference uniformity solution of reference stations of railways and shipping lanes. This is significant to the coordinates uniformity and regular updating maintenance of high-precision service datum network of railways.
How to cite: Wang, X., Wu, J., Zhang, P., and Liu, C.: Research on the Method for Realizing Datum Uniformity of Cross-region Reference Stations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8611, https://doi.org/10.5194/egusphere-egu26-8611, 2026.
Significant spatial heterogeneity of tropospheric water vapor over the Tibetan Plateau, particularly along the vertical dimension, presents a major challenge to traditional tropospheric wet delay models used in GNSS applications across large height ranges. To address this issue, we developed a refined Tibetan zenith wet delay (ZWD) model based on pressure-level ERA5 reanalysis data with a spatial resolution of 0.25°, providing ZWD estimates at arbitrary times and heights. The model adopts an improved vertical correction in which ZWD vertical profiles are represented by a cubic polynomial height-correction function. The polynomial coefficients are estimated at each ERA5 grid node using ERA5-derived ZWD profiles and are further parameterized using harmonics up to the semidiurnal term to characterize temporal variability. ZWD at user locations is obtained through bilinear interpolation of the four surrounding grid nodes, ensuring continuous spatial and vertical coverage over Tibet. Validation using ERA5- and radiosonde-derived ZWDs shows that the TZ model achieves lower bias and reduced root-mean-square error (RMSE) than the widely used GPT3 model at both surface and elevated layers. These results indicate stable performance of the model across the full altitude range. The proposed model can be readily integrated into GNSS positioning frameworks. In precise point positioning (PPP), it may be introduced as a virtual observation of ZWD, with the model RMSE used to define the initial measurement-noise covariance. As the model provides a priori information rather than true observations, a time-varying down-weighting strategy is applied so that the ZWD estimation progressively relies on actual GNSS observations. In network real-time kinematic (NRTK) applications with large height differences, model-derived ZWD combined with mapping functions can be used to mitigate height-dependent double-differenced tropospheric delay residuals. This improves positioning accuracy, especially in the vertical component.
How to cite: Liu, C., Wu, J., Wang, X., and Xu, C.: A refined tropospheric zenith wet delay model for GNSS applications over the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9198, https://doi.org/10.5194/egusphere-egu26-9198, 2026.
Within the TRANSFORM² project, seismic events have been simulated on a triaxial earthquake shake table (ANCO R-303) during a series of ten experiments conducted at the Seismological Observatory in Timișoara, Romania, in October 2025. These experiments included 223 variants of motion, consisting of either fully synthetic or natural records of earthquakes occurring in Poland (2019 mining induced seismicity), Italy (2016 Norcia seismic event), Romania (Vrancea 1986 earthquake) and Greece (2020 Samos and 2021 Damasi earthquakes). The tabletop was instrumented with GNSS receivers, including both low-cost and geodetic-grade – some of which were connected to external atomic clocks, accelerometers of both low-cost and professional types, and inclinometers, providing high-resolution, multi-sensor observations. Multiple configurations of the equipment setup, instrument settings, and data processing options.
We introduce a fully documented and organized dataset that will be shared publicly on Zenodo. This presentation discusses an experimental setup, sensor calibration, and integration workflow to combine multi-sensor measurements into one coherent dataset. Several illustrative analyses are included: the comparisons of low-cost versus high-grade GNSS and accelerometer data; preliminary processing results assessment, and visualizations of induced motions across various motion variants.
The provided dataset will enable the geoscience community to validate algorithms for high-frequency GNSS data, accelerometric data, and fusion algorithms, but also related to seismological modeling, ranging from controlled robotic experiments to real-world seismological data.
Acknowledgments: TRANSFORM² is funded by the European Union under project number 101188365 within the HORIZON-INFRA-2024-DEV-01-01 call.
How to cite: Kudłacik, I., Kapłon, J., Nastase, I. E., Tiganescu, A., Elias, P., Kaczmarek, A., Katakonstantis, A., Nistor, S., Galvi, S., Fabris, P., Zuliani, D., and Marmureanu, A.: High-Rate GNSS and Multi-Sensor Benchmark Dataset from Earthquake Simulation Experiments driven by EPOS NFO Community (TRANSFORM²), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12065, https://doi.org/10.5194/egusphere-egu26-12065, 2026.
The increasing demand for Earth science applications presents challenges in enhancing geodetic reference frames. Systematic errors currently restrict the accuracy of these frames, as traditional geometric connections between various space-geodetic techniques are inadequate. To tackle this issue, the DFG-sponsored project FOR5456 aims to reduce systematic errors by utilizing clock ties, including the integration of optical clocks into Space Geodesy.
Recent developments in optical clocks have achieved frequency instabilities below 1×10⁻¹⁵ at 1 s integration time. While this level of short-term stability is beyond the immediate needs of space-geodetic instruments, optical clocks offer substantial benefits for the long-term stability of timing signals.
In particular, the long-term coherence of GNSS time transfer can be improved by calibrating receiver-induced phase instabilities. GNSS carrier-phase measurements do not directly represent the phase of the input clock signal, as they are affected by variable delays inside the receiver. This calibration is enabled by an optical delay-stabilized timing system developed at the Geodetic Observatory Wettzell, which provides a highly stable and well-defined phase reference. Based on this infrastructure, we have developed a real-time GNSS phase calibrator that generates a pilot signal synchronized with the phase of the input clock. This pilot signal is then used terrestrially to remove receiver-induced phase instabilities.
GNSS is currently the only continuously operating space-geodetic system capable of continuous comparison of clocks at the 10⁻¹⁸ level and beyond. However, achieving this requires careful mitigation of receiver instabilities is essential. This contribution presents the design of the GNSS phase calibrator, its synchronization procedure with the clock signal, and an analysis of its performance in terms of long-term time and phase stability, enabling future high-precision clock comparisons over extended time scales.
How to cite: Kodet, J., Kimer, M., Wang, Z., and Pany, T.: Towards Clock Ties in GNSS: A Real-Time Phase Calibrator for Receiver Instability Mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16284, https://doi.org/10.5194/egusphere-egu26-16284, 2026.
Noise characteristics of GPS time series have been documented well so far. Nowadays, researchers study the behavior of the noise from GNSS time series. The examples are yet so few in which the spectral behavior of the noise and differences from the GPS solutions are assessed. For this aim, we are testing our in-house time-series analysis software using GPS solutions from the JPL’s GPS time-series archive. The software is being developed in Yildiz Technical University. GNSS data, which is obtained form the IGS’s MGEX experiment, have been processed using PRIDE GNSS software developed by Wuhan University. About 50 globally well scattered MGEX GNSS stations which are capable of acquiring data from 2-5 different GNSS techniques have been selected. Robust estimators are used to remove outliers and data gaps are filled with AR(1). We do not use large matrix computations while estimating the spectral index, learning lessons from the predecessors of the area, rather an iterative and computationally efficient algorithm is used. Alternative algorithms to automatically fix the offsets which are problematic in trend and velocity uncertainty estimation are also offered during the analysis. We calibrate our velocity estimations with those of the GNSS time-series solutions produced by the JPL using GIPSY-X. Initial results seem to be promising. With the contribution of various GNSS techniques (e.g. GALILEO, GLONASS, GPS, BeiDou, etc.) to the geodetic time series analysis the value of the spectral indice changes and the magnitude of the velocity uncertainty on the average becomes smaller. Namely, the noise shrinks and becomes whiter. The velocity uncertainty is reduced by about 11% for the East and 13% for the Up components while the north component stays neutral.
How to cite: Sanli, D. U., Cetin, D., and Erçoban, M.: Change in spectral index and improvement in velocity uncertainty due to MULTI-GNSS contribution to geodetic site velocity estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17422, https://doi.org/10.5194/egusphere-egu26-17422, 2026.
As interest grows in leveraging Low Earth Orbit (LEO) satellites in positioning, navigation, and timing (PNT), characterizing ranging errors for LEO satellite signals has become essential for the system design. The ionospheric group delay affecting PNT signals, which causes significant positioning errors, is proportional to the total electron content (TEC) along the signal path. Existing Global Navigation Satellite System (GNSS) ionospheric models (e.g., Klobuchar, NeQuick) provide ionospheric corrections by using broadcast model parameters, enabling real-time estimation of slant TEC (STEC). However, conventional GNSS ionospheric models were primarily developed and validated for the full slant path to GNSS satellites. Thus, they are not directly appliable to LEO satellites, which operate at much lower altitudes than GNSS constellations.
Accurate TEC estimation for LEO PNT requires precise vertical electron density profiles to account specifically for the ionosphere below the LEO satellite. The NeQuick-G model, developed for Galileo single-frequency users, provides a three-dimensional electron density distribution based on ionospheric parameters of CCIR (International Radio Consultative Committee) numerical map and broadcast effective ionization level (Az) parameters. Several studies have investigated the validity of the NeQuick-G model in estimating partial TEC above LEO satellites (topside ionosphere) for spaceborne applications. Montenbruck & Rodriguez (2020) evaluated its performance for LEO satellite onboard orbit determination using GNSS measurements from Swarm LEO satellites orbiting at mean altitudes of 480 km and 520 km. Oezmaden et al. (2025) extended this analysis to multiple LEO constellations across different altitudes and developed a residual error model for the NeQuick-G model in the topside ionosphere. However, for LEO PNT applications, the NeQuick-G model should be validated for the bottomside ionosphere below the LEO satellite. This study analyzes the residual error of the NeQuick-G model for bottomside STEC by differencing STEC observations from ground receivers and LEO satellite onboard receivers. The bottomside STEC can be estimated by differencing these observations when the ground receiver, LEO satellite, and GNSS satellite are geometrically aligned. Using position data from IGS stations, GNSS, and Swarm satellites, we searched for alignment events for 2019 and 2024 and derived the bottomside STEC. These bottomside STECs are then compared with NeQuick-G model estimates using Az parameters broadcast in Galileo navigation messages.
Residual errors are analyzed under different ionospheric conditions such as solar activity, geomagnetic latitude, and local time. The standard deviations of the residual errors are 10.62 TECU during the solar maximum (2024) and 4.47 TECU during the solar minimum (2019), satisfying Galileo target specification for residual STEC errors (68% probability within 20 TECU or 30% of STEC). The variability of residual error is consistently higher in low latitude regions than at mid or high latitudes, indicating increased model uncertainty associated with equatorial ionospheric dynamics. These findings provide empirical validation of the NeQuick-G model for bottomside ionospheric correction in emerging LEO PNT applications.
References
Montenbruck,O., González Rodríguez,B.(2020).NeQuick-G performance assessment for space applications. GPS Solut 24, 13 https://doi.org/10.1007/s10291-019-0931-2
C.Oezmaden, S.Pelzer, O.G.Crespillo, M.Brachvogel, M.Niestroj and M.Meurer.(2025).Residual GNSS Ionospheric Error Analysis in Future Low Earth Orbit Applications. 2025 IEEE/ION Position, Location and Navigation Symposium(PLANS), doi:10.1109/PLANS61210.2025.11028459.
How to cite: Do, J.-M., Nam, G., Sun, A. K., and Lee, J.: Residual Ionospheric Errors of the NeQuick-G Model for LEO PNT , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18088, https://doi.org/10.5194/egusphere-egu26-18088, 2026.
A major limitation of using UAV photogrammetry for coastal erosion and geohazard assessment is the effort associated with GCP-based georeferencing. To optimize this, we evaluate the German national PPP-RTK (precise point positioning real-time kinematic) service GEPOS®, provided by the Federal Agency for Cartography and Geodesy (BKG). The focus is on its ability to enable repeatable, GCP-free, and more time-efficient UAV-derived 3D products, suitable for operational upscaling.
In total, we conducted 42 UAV surveys with three platforms (two DJI Mavic 3 Multispectral; RGB imagery only, and one DJI Mavic 3 Enterprise) in 10/2025, covering both nadir-only and multi-view oblique acquisition geometries at an actively eroding cliff section near Wustrow (Baltic Sea coast). Here a former East German military bunker collapsed in February 2024, thus providing a benchmark. GEPOS® corrections were generated by the BKG using three dedicated access points located within the area of interest and at distances of approximately 350 m and 1000 m. This setup enabled an initial sensitivity assessment of repeatability as a function of the correction-access configuration.
In the absence of independent geodetic reference, we focused on repeatability (relative precision). To this end, we compared point clouds and DSMs across near-contemporaneous surveys to avoid impact of surface change. We applied a two-stage evaluation strategy: (i) we quantified end-to-end repeatability from unaligned model-to-model differences, reflecting combined georeferencing and photogrammetric effects; and (ii) removed rigid offsets by masked ICP-based co-registration on a stable concrete reference surface, transferring the derived rigid-body transform to the entire dataset before reassessing residual differences. We assessed distances using M3C2 and robust summary statistics such as Normalized Median Absolute Deviation (NMAD) and 95th and 99th percentiles of absolute M3C2 distances after excluding change-prone areas.
Preliminary results indicate that oblique acquisition achieves centimeter-level repeatability without co-registration and improves to around the centimeter scale after co-registration. In contrast, nadir-only surveys show substantially larger inter-model discrepancies prior to co-registration, consistent with predominantly rigid offsets, but converge to low-centimeter residuals after alignment on stable surfaces. The final analysis will quantify repeatability across platforms, acquisition geometries, and correction-access configurations, and evaluate the implications for scaling UAV-based coastal monitoring while minimizing field effort.
How to cite: Anschütz, H., Torizin, J., Schüßler, N., Reschke, P., Schirrmann, T., Fuchs, M., Schütze, K., Rost, C., Neumaier, P., and Mohr, C. H.: Towards scalable, GCP-free UAV photogrammetry using PPP-RTK: repeatability tests at a Baltic Sea cliff site (Wustrow, Germany), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18153, https://doi.org/10.5194/egusphere-egu26-18153, 2026.
The ionospheric irregularities of the plasma density could significantly cause fluctuations of the intensity, phase, angle of arrival, and polarization state of trans-ionospheric GNSS signals in a rapid and stochastic manner, i.e., ionospheric scintillation, which could lead to communication errors and signal distortions, further seriously threatening the security of the GNSS system and affecting the performance of GNSS high-precision positioning services. Therefore, the mitigation of its impact on the GNSS high-precision positioning service has been a key scientific issue in the field of satellite navigation.
In this study, we proposed a TurboEdit cycle slip detection threshold adjustment method that considers scintillation differences at different latitudes, and constructed a stochastic model considering the ionospheric scintillation information. Through the distribution of cycle slips with regard to the scintillation index, the cycle slip threshold method is initially established for various latitudes; the ionospheric scintillation index calculated from the geodetic GNSS observations is used to construct a stochastic model, which can assign more reasonable weights for the GNSS observations affected by the ionospheric scintillation. Results show that compared with the empirical cycle slip detection threshold, the adjusted cycle slip detection threshold can significantly improve the performance of PPP positioning results in various regions under ionospheric scintillation conditions. Compared with the elevation-angle stochastic model, the improved stochastic model can mitigate the effects of the ionospheric scintillation on PPP solutions in the equatorial ionization anomaly region. This study contributes to enhancing the accuracy, reliability, and integrity of high-precision GNSS applications and services.
How to cite: Liu, H., Ren, X., Chen, P., and Zhang, X.: Leveraging Latitude-Differentiated Thresholds and Weight Optimization to Mitigate the Impacts of Ionospheric Scintillation on GNSS PPP, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20628, https://doi.org/10.5194/egusphere-egu26-20628, 2026.
The Dunaszekcso landslide is one of the major landslides in Hungary located at the right bank of the Danube in the southern part of the country. The landslide shows a retreat of 5-15m per 100 year in the past 2000 years according to geological studies. In this area, the high riverbanks recently experienced major slides, which damaged houses and linear structures. The area has been being monitored for several years using InSAR technique and static GNSS observations, but due to the low temporal resolution of the static GNSS observation campaigns and the lack of frequent absolute displacement control, the InSAR data processing sometimes face significant challenges.
To overcome this problem, we teemed up with European universities, geological services and companies in the GeoNetSee (GeoNetSee – ‘An AI/IoT-based system of GEOsensor NETworks for real-time monitoring of unStablE tErrain and artificial structures’) project (Interreg Danube Region programme, no: DRP0200783), which focuses on developing and testing innovative geodetic approaches for monitoring slope instabilities and infrastructure-related geohazards. Within the GeoNetSee framework, low-cost GNSS receivers are installed at selected slopes, and time series from variometric processing are analysed to identify subtle dynamic and quasi-static displacements caused by environmental loading, rainfall-induced pore pressure changes, and other disturbances. GNSS-derived signals are validated against complementary monitoring methods, including InSAR, traditional geodetic surveys and environmental sensors.
The primary objective of this study is to assess the potential of various GNSS-based observation processing strategies (RTK, fast static, variometric) to detect early deformation signals that may precede landslide events. Low-cost permanent and monitoring GNSS stations were placed on the steep slopes and on the stable ground to monitor the geometrical changes of the slopes and the GNSS observations are analysed with different measurement models and compared to the results of InSAR analysis.
Preliminary results demonstrate that low-cost GNSS techniques are valuable means to detect small-amplitude, short-term slope deformations, indicating changes in stability conditions. These findings highlight the method’s potential as part of integrated slope monitoring and early-warning systems, offering continuous, autonomous, and real-time data that can enhance landslide risk assessment and mitigation strategies in climate-sensitive regions of Central Europe and beyond.
How to cite: Rozsa, S., Turák, B., Kis, A., Bozsó, I., and Török, Á.: Landslide monitoring using permanent low-cost GNSS sensors and InSAR techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21937, https://doi.org/10.5194/egusphere-egu26-21937, 2026.
High-precision GNSS services typically rely on dense reference networks to model atmospheric delays effectively. However, maintaining such infrastructure is often impractical in vast or developing regions. This study evaluates the feasibility of Precise Point Positioning with Ambiguity Resolution (PPP-AR) using a minimal network configuration consisting of only four Continuously Operating Reference Stations (CORS).
We focus on quantifying the spatial degradation of positioning accuracy as the distance from the reference network increases. By generating corrections from this sparse cluster, we analyze performance metrics including convergence time, fixing rate, and coordinate precision across rovers located at varying baselines from the network centroid.
The results demonstrate a clear correlation between distance and accuracy degradation, specifically highlighting the impact of residual ionospheric and tropospheric errors. Despite this degradation, the study confirms that PPP-AR can maintain reliable centimeter-level positioning well beyond the limits of traditional Network RTK (NRTK). These findings provide empirical guidelines for deploying cost-effective GNSS infrastructure with optimized station density.
How to cite: Skakun, I., Abanosimov, V., Sviridov, A., and Suvorkin, V.: Validating PPP-AR Performance in Sparse Infrastructure: A Case Study with Four Reference Stations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21941, https://doi.org/10.5194/egusphere-egu26-21941, 2026.
In order to meet the demanding accuracy and availability requirements, GPS has introduced L5 signals that are compatible with Galileo E5a signals. These signals are designed to mitigate multipath issues and poor performance in challenging environments such forests and areas affected by jamming. As they are also intended to replace L2 signals in the future, steps must be taken to exploit the modern signal type that is currently broadcast by 20 GPS satellites of blocks IIF and III. This is considered particularly important for some LEO satellites, which rely exclusively on L1/L5 observations.
In addition to standard analysis products based on L1/L2, the CODE (Center for Orbit Determination in Europe) IGS Analysis Center is in the process of testing a prototype processing chain to generate L1/L5-based products, paving the way for GNSS processing to be fully based on L1/L5 signals. The presentation addresses the application of these products to LEO orbit determination and PPP processing of the ground stations, considering different antenna calibrations for IIF satellites. A quantitative validation and comparison with L1/L2-based solutions is also discussed.
How to cite: Kalarus, M., Schaer, S., Dach, R., Arnold, D., and Jaeggi, A.: Analysis of experimental CODE products based on GPS L1/L5 signals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20376, https://doi.org/10.5194/egusphere-egu26-20376, 2026.
