Poster session
Although Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has been extensively utilized for monitoring water levels in various bodies of water such as lakes, rivers, tides, and storm surges, its capability in detecting high-dynamic phenomena like tsunamis is still largely unexplored. This research introduces a near-real-time GNSS-IR tsunami monitoring framework, which concurrently inverses sea level, vertical sea-level velocity, and acceleration with the employment of an adaptive window strategy. Through this framework, we observed the tsunami wave of 2020 Tonga tsunami based on GNSS observations alone, with correlation of 62.9% compared with tide gauge, showing that GNSS-IR can detect tsunami waves with an amplitude of ~0.5 meters and a period of ~40 minutes. Additionally, it is discovered that the observation conditions necessary for tsunami monitoring are more stringent compared to those for tidal measurements, with a minimum requirement of six available satellite arcs (half rising and half setting) within a one-hour window. Consequently, out of 84 GNSS sites evaluated along the Pacific Rim, 29 are identified as being capable of conducting effective tsunami monitoring, whereas the remaining sites are only suitable for observing tides and storm surges.
How to cite: Chai, H., Chen, K., and Lin, J.: Transforming coastal GNSS stations into tsunami gauges with GNSS-IR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3945, https://doi.org/10.5194/egusphere-egu25-3945, 2025.
The MAGIC-CH project aims to advance the application of environmental products for climatological monitoring derived from observations of Global Navigation Satellite Systems (GNSS) Interferometric Reflectometry (IR). The project explores the potential of the existing Swiss GNSS infrastructure for monitoring essential climate variables, including snow, soil moisture, and atmospheric water vapor.
In this contribution, we aim to apply machine learning techniques to directly retrieve soil moisture and snow heights from GNSS-IR observables. Time series of signal-to-noise ratio (SNR) from ground-reflected GNSS signals are utilized as features, while satellite-based soil moisture data and in-situ snow height observations serve as target variables. Additionally, azimuth and elevation angle, day of the year, and a digital elevation model are used as inputs in the machine learning framework.
Preliminary results for soil moisture retrieval are based on the XGBoost algorithm, using GNSS data from the Automated GNSS Network for Switzerland (AGNES) and the 1 km surface soil moisture product provided by the Copernicus Global Land Monitoring Service. For snow height, initial results are based on an artificial neural network, GNSS-IR measurements of the Plate Boundary Observatory, and snow height observations of SNOTEL sites. The performance of these machine learning models shows promising improvements, significantly reducing standard error measures compared to traditional retrieval methods.
How to cite: Crocetti, L. and Aichinger-Rosenberger, M.: MAGIC-CH: Machine Learning-based Advancement and Usability Assessment of GNSS Interferometric Reflectometry for Climatological Studies in Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8297, https://doi.org/10.5194/egusphere-egu25-8297, 2025.
Sea level is a critical environmental parameter with significant implications for both daily life and global environmental processes. Traditionally, coastal sea levels have been measured using tide gauges. However, tide gauge measurements are affected by the vertical land motion and some sites lack a consistent reference datum on a global scale. GNSS Interferometric Reflectometry (GNSS-IR) offers a promising alternative for continuous coastal sea level monitoring within a geodetic reference frame, supporting sea level studies on a global scale. Despite its potential, GNSS-IR is hindered by various error sources that can degrade and bias sea level measurements. Achieving both precise positioning and accurate sea level monitoring simultaneously remains a significant challenge. To enhance the accuracy of GNSS-IR sea level measurements and align the results within a geodetic reference frame, we developed a dual-antenna GNSS reflectometry and positioning system. The system features a zenith-pointing GNSS antenna optimized for positioning and a horizon-pointing antenna designed for sea level monitoring. These two antennas are tightly connected, with precise measurements of the vertical distance between them. Advanced GNSS-IR error models, including antenna phase center corrections, are incorporated to mitigate errors in the sea level retrievals. The performance of this system was evaluated using nearby geodetic GNSS installations, traditional tide gauge measurements, and leveling surveys. Initial results demonstrate that the zenith-pointing antenna achieves sub-centimeter level positioning precision, comparable to nearby GNSS reference stations. The horizon-pointing antenna provides sea level measurements with an 1σ error of approximately 2 cm on a temporal resolution of 15 min. Notably, the zenith-pointing antenna performs significantly worse for sea level retrieval, while the horizon-pointing antenna is less accurate for precise positioning. These findings highlight the necessity of a dual-antenna system for achieving optimal performance in both positioning and sea level retrieval. The developed system enables continuous coastal sea level monitoring within the GNSS reference frame with centimeter-level accuracy, offering a valuable dataset contributing to global sea level studies.
How to cite: Peng, F., Haas, R., and Elgered, G.: Continuous coastal sea level measurement in the global geodetic coordinate frame: A realization with GNSS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8758, https://doi.org/10.5194/egusphere-egu25-8758, 2025.
As part of the Collaborative Research Center (SFB 1502) funded by the German Research Foundation (DFG), a project is being carried out to analyze surface water levels and discharge using data from the latest generation of satellite altimetry. Within this project, a network of eight Raspberry Pi Reflectors (RPR) (Karegar et al. 2022, WRR) was strategically installed in the middle Rhine valley and upper Rhine along a stretch of about 110 km during the spring and summer of 2023. While the primary goal of this deployment was to validate SWOT (Surface Water and Ocean Topography) surface water level observations, four RPRs were also placed under Sentinel-3 tracks A156 and B156. Sentinel-3 L1A data were acquired and processed using the Fully Focused SAR (FFSAR) processor and automatic off-nadir processing method (Chen et al. 2025, J. Hydrology). At Worms, multiple in-situ sensors are being used. The river gauge maintained by the German Federal Waterways and Shipping Administration (WSV) records water levels at 15-minute intervals. It is a classic float and stilling well gauge located on the riverbank and connected to the water via an underground pipe. A commercial radar sensor from Vortex-io was mounted on a bridge overlooking the river. Also, an RPR was installed in Worms to evaluate its performance. Having these three sensors based on different techniques allows us assess their consistency together with Sentinel-3 observations. This presentation particularly reports on the RPR’s behavior and long-term performance in off-grid regions where there is no grid coverage or local power supply. We will also discuss their application in validating Sentinel-3 data, especially in the steep and narrow Middle Rhine Valley where the surrounding terrain significantly influences the accuracy of both GNSS-IR and Sentinel-3 measurements.
How to cite: Karegar, M., Chen, J., Fenoglio-Marc, L., and Kusche, J.: Performance of Raspberry Pi Reflectors and multiple in-situ sensors for surface water monitoring and sentinel-3 validation over two years, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17647, https://doi.org/10.5194/egusphere-egu25-17647, 2025.
Sea level monitoring is of great significance for studying global climate change, disaster monitoring, and water resource management. GNSS Interferometric Reflectometry (GNSS-IR) technology is considered an effective complement to traditional sea level monitoring methods and has gained significant attention in recent years. However, in the two main GNSS-IR methods for sea level retrieval (spectral analysis and inverse modeling), real-time performance is limited because each low-elevation angle trajectory corresponds to a single retrieval value, and post-processing is required to eliminate outliers. In this study, we propose a near-real-time sea-level retrieval method based on shared-frequency signals. A sliding time window was used, and different strategies were employed to combine dSNRs from various satellites within the time window using both spectral analysis and inverse modeling processing methods to accurately extract the reflector height, which was further converted into sea level. Through experiments conducted at two PBO stations, we verified that near-real-time spectral analysis and inverse modeling methods can stably output sea-level retrievals within 20/30-minute time windows. Subsequently, a comparison with the traditional method revealed that the near-real-time approach enables sea-level monitoring with higher accuracy and much more uniform time resolution. This study shows that GNSS-IR technology can achieve near real-time, high-precision sea-level monitoring in multi-system scenarios and further promotes its application in sea-level monitoring.
How to cite: Martin-Furones, A. and Liu, S.: GNSS-IR near real-time sea level retrieval from multi-constellation shared-frequency signals, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6360, https://doi.org/10.5194/egusphere-egu25-6360, 2025.
Sea level monitoring is vital for applications such as urban planning and climate studies. Traditional tide gauges, which often use radar measurements, remain the most common method but require placing sensors near the water surface, exposing them to potential damage in hostile maritime environments.
The demand for precise and high-frequency sea level monitoring has led to the development of innovative techniques, including Global Navigation Satellite System Reflectometry (GNSS-R). This promising method enables continuous, global sea level measurements (e.g., Larson et al., 2013) without the limitations of traditional sensors such as underwater (pressure gauges), in contact (float gauges), or above-surface (acoustic and radar). Furthermore, it takes advantage of the high availability of existing GNSS installations around the globe, potentially expanding the scope of tide gauge measurements on a global scale.
GNSS-R measurement methods are typically based on the analyze of the Signal-to-Noise Ratio (SNR) and most of the applied methods are using spectral analysis, such as Lomb-Scargle periodograms, to achieve centimeter-level accuracy in average sea level estimation (Larson, Ray and Williams, 2017). However, these methods require long SNR time series, limiting their ability to capture high-frequency sea level variations. This as the effect to restricted spectral methods to observing slow sea level variations, such as tides. Recent advancements have demonstrated that Kalman filtering can achieve sub-5 cm accuracy while increasing data sampling rates (e.g., Santamaría-Gómez and Watson, 2017; Strandberg, Hobiger and Haas, 2019; Liu et al., 2023).
We present a novel approach to enhance the accuracy and temporal resolution of sea level observations from SNR analysis, using an Extended Kalman Filter (EKF) to estimate SNR oscillation frequency and amplitude. The main advantage it to abble to ofer a flexibility to select the sampling rate of the resulting time series, allowing sea level estimates at rates up to the original SNR measurement frequency.
Our approach integrates data from all available GNSS constellations and carriers, improving data redundancy and robustness, particularly during signal loss or satellite transitions. Additionally, we apply a correction for phase center offset (PCO) variations, specific to each carrier and antenna model. This ensures that the resulting water height time series is referenced to a precise physical point, the Antenna Reference Point (ARP).
We validated our method by reconstructing a year-long sea level time series at Brest, France, with a 10-second sampling interval. We finally achieved a root-mean-square-error (RMSE) of 2.6 cm compared to the data of a co-located radar tide gauge. Similar performance was observed at other sites, with sampling rates as high as 1 second. Furthermore, the algorithm's ability to track irregular and unpredictable sea level variations beyond tidal models was evaluated during 10 days of observation at Cedar Key (Florida, USA) during Hurricane Milton. The resulting water height time series showed an RMSE of 1.9 cm compared to the data of a co-located tide gauge, demonstrating the method's efficiency under complex hydrodynamic conditions.
How to cite: Pira, A., Santamaría-Gómez, A., and Wöppelmann, G.: Extended Kalman filtering for high-rate GNSS-R sea level measurements : Achieving centimeter-level precision during Hurricane Milton at Cedar Key, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19433, https://doi.org/10.5194/egusphere-egu25-19433, 2025.
Sea level estimations have an important influence on climate research, marine transportation, production and life of coastal people and so on. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) has been verified as a promising alternative of remote sensing technique for coastal sea level monitoring. Signal-to-noise ratio (SNR) data received by one off-shelf antenna and a classic GNSS geodetic receiver, which contain the multipath information of the station environment, were usually used for sea level retrieval. However, due to its use of only observations at low satellite elevation angles, this GNSS-IR technique is limited by its low sampling rate, for which only one sea level retrieval can be obtained for each independent satellite ascending or descending arc. In this study, we proposed to use an improved GNSS-IR setup with two antennae to improve the sampling rate by realizing sea level estimation from both high- and low- elevation angle observation. Compared to the traditional GNSS station with one upward right-hand circular polarized (RHCP) antenna and one geodetic receiver, this new setup added one downward left-hand circular polarized (LHCP) antenna and one combiner. Observations of the two antennae combined by the combiner were input to the geodetic receiver to output RINEX files. SNR data from the RINEX files were then used to estimate sea level. One-month coastal experiment using the new setup was performed from January 4, 2023, to February 5, 2023, in Weihai, China. By comparing with co-located tide gauge data, sea level estimations with RMSE of 7 cm and correlation coefficient of 0.99 were obtained from the experimental data obtained by using the proposed setup. Moreover, the results showed that, by using the proposed GNSS-IR setup, the sampling rate of sea level estimations was improved about 3 times comparing to the results of conventional setup with one antenna. Because of the cheap price of LHCP antenna and combiner, the proposed GNSS-MR setup can improve its sampling rate of retrievals while maintaining its low-cost advantage. This new setup could be applied to other GNSS-IR sites and is therefore beneficial for global sea level monitoring.
How to cite: Wang, N., Gao, F., Xu, T., Meng, X., He, Y., and Wang, M.: Sea Level estimations based on GNSS-IR with two antennas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16400, https://doi.org/10.5194/egusphere-egu25-16400, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot 1
EGU25-3157 | ECS | Posters virtual | VPS23
Improving near real-time GNSS-IR sea level retrievals with robust outlier detectionThu, 01 May, 14:00–15:45 (CEST) vPoster spot 1 | vP1.28
The Global Navigation Satellite Systems - Interferometric Reflectometry (GNSS-IR) method has been utilized for nearly fifteen years as an alternative and cost-effective approach to determine hydrological parameters such as sea level, snow depth, and soil moisture through the analysis of signal-to-noise ratio (SNR) data. Most GNSS-IR studies to date rely on archived data and post-processed results. However, the potential for near real-time GNSS-IR analysis is increasingly being explored. In this study, high-rate GNSS archive data, sampled at 1-second intervals and stored in 15-minute files, were processed in a simulated near real-time workflow. Every 15 minutes, new data were added to the analysis, focusing exclusively on the most recent 60 minutes of observations. A novel approach for detecting outliers in near real-time GNSS-IR estimates was also proposed. The median-based robust outlier detection (ROD) method, previously validated for post-processed GNSS-IR snow depth results, was adapted and applied to near real-time GNSS-IR data. A 30-day dataset of multi-GNSS, multi-frequency SNR observations from the Portland (PTLD) GNSS station in Australia, collected in November 2024, was analyzed. The near real-time GNSS-IR results were validated using sea level measurements from the PORL tide gauge station. The results demonstrate that the modified ROD approach can be used to identify outliers in near real-time GNSS-IR sea level retrievals.
How to cite: Altuntas, C., Erdogan, B., Tunalioglu, N., and Williams, S.: Improving near real-time GNSS-IR sea level retrievals with robust outlier detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3157, https://doi.org/10.5194/egusphere-egu25-3157, 2025.
