Global Navigation Satellite System Data Research Articles

Motion Analysis Using Global Navigation Satellite System and Physiological Data

Motion analysis by wearable sensors forms a very important research topic with a general mathematical background and applications in different areas including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body and the simultaneous acquisition of signals from further sensors. The application is related to the monitoring of physical activity and the analysis of the heart rate dynamics during the run at route segments of different slopes with changing speed, forming an alternative to the heart monitoring on the treadmill ergometer. The proposed computational method includes digital methods of data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The detection of the patterns of motion includes segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This method of classification provides a comparison of neural networks, support vector machine, Bayesian, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface suggest the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems as well.

Multilayer Ionospheric Model Constrained by Physical Prior Based on GNSS Stations

The need for accurate modeling of the ionosphere plays an important role in the global navigation satellite system (GNSS) positioning. The traditional multi-layer VTEC model without prior has been used for modeling the ionospheric delay error. However, it is assumed that the electron density of the ionosphere is compressed into multiple thin layers at fixed heights in the lack of capturing ionospheric physics. In this paper, the data enhancement method by virtual observations is proposed to build the constrained multi-layer VTEC model to capture physical features from empirical ionospheric models. The extraction methods of physical knowledge have been developed by prior VTEC based on principal component analysis and model coefficients based on emulated basis function. The constrained multi-layer modeling has been verified based on simulation and real measurement of GNSS data in Yunnan, China collected from Qianxun Ground-based Augmentation System on November 3, 2021. The receiver DCB error estimated by the multi-layer model with prior constraint is significantly lower than that of the single-layer model and the traditional multi-layer model. The experimental test shows that the constrained multi-layer model achieves the accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.5 \,\rm {TECU}$</tex-math></inline-formula> for the independent reference station. The dSTEC of the proposed two multi-layer models are significantly lower than those of the single-layer model for low elevation angles, and the RMSE of dSTEC is reduced by 63 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> with the cutoff elevation angle of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{\circ }$</tex-math></inline-formula> . The spatial distribution of the multi-layer VTEC model shows consistency with the tomography model to verify vertical feature capturing capability. Compared with the Un-differenced and Un-combined Precise Point Positioning (UCPPP) without ionospheric constraint, the multi-layer constrained model based on the test data improves the convergence time approximately by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$36.55\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$18.78\%$</tex-math></inline-formula> in the horizontal (H), up (U) directions, respectively. These results demonstrate that the proposed multi-layer models not only improve ionospheric delay estimation precision but also can obtain the VTEC distribution capturing the physical characteristics of the ionosphere. The proposed multi-layer models may be valuable for the ionospheric delay modeling of satellite navigation systems in harsh variable ionospheric conditions.

Tropospheric parameters from multi-GNSS and numerical weather models: case study of severe precipitation and flooding in Germany in July 2021

Storms and floods are the most frequent natural disaster in western and central Europe. Due to climate change, intensive storms with prolonged rain episodes will continue to cause even more destructive flooding. The good understanding and forecasting of such events are thus of uttermost importance. One of the ways to improve weather forecasts is the assimilation of external data, such as the global navigation satellite systems (GNSS). In this study, a preparation of the multi-GNSS tropospheric products—zenith total delays, tropospheric gradients and slant total delays—for future operational assimilation is shown. For a severe precipitation event in July 2021 in Germany, the GNSS parameters from three systems—GPS, GLONASS and Galileo—are compared to three Numerical Weather Models (NWMs)—ERA5 reanalysis of ECMWF, ICON run by the German Weather Service and GFS run by the US Weather Service. The flood that followed the rainfall was the deadliest natural disaster in Germany since 1962. The results show that all considered GNSS solutions have a similar level of agreement with the NWMs. However, for the flood region in western Germany, the biases from the multi-GNSS solutions are smaller compared to the GPS-only solution. From the models, ICON has the highest agreement with the GNSS data for all considered tropospheric parameters. The best agreement with the ICON is probably due to its high horizontal resolution and, thus, low representative errors and the fine tuning of DWD’s regional model for the specific region (Germany).

Derived precipitable water vapour from GNSS and radiosonde data using time series and spatial least-square

ABSTRACT Precipitable water vapour (PWV) plays an important role in rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In this paper, radiosonde techniques are used to compute PWV’s spatial and temporal variations and GNSS (Global Navigation Satellite Systems) using in spatial only. GNSS data (GPS and GLONASS) from eight Egyptian stations were processed for the year 2014. Five radiosonde stations for the period from 2005 to 2016 were used. Time series is constructed using the daily surface measurements of radiosonde stations. The linear trend is estimated by straight line fit over 12 years of seasonally adjusted PWV time series. The PWV in Egypt has a positive trend in time series at more than five radiosonde sites with a rate of 0.3 mm/year. The monthly cycle is a near sine curve and the stochastic errors are from 0% to 5.4% over 12 years. The comparison between PWV estimated from GNSS data using the PPP approach and radiosonde data for each station in year 2014 was done in the near station. The nearest two stations, GNSS station “MTRH” and radiosonde station “62,306”, get a bias of 0.66 mm. Three common interpolation techniques (Inverse Distance Weighting, Kriging, and Minimum Curve) are used. The biases of the three used methods were 1.65 mm, 1.96 mm and 0.61 mm, respectively. The statistical methods of Minimum Curve interpolation are found superior to other methods with mean error at Mersa-Matrouh, Aswan and Al-Arish stations reaching 0.1 mm, 1.0 mm and 0.30 mm, respectively. The minimum curve technique is recommended in spatial interpolation for the prediction of PWV amount.Abbreviations: PWV: precipitable water vapour; PPP: precise point positioning; GNSS: global navigation satellite system; ZPD: tropospheric zenith path delay; ZWD: zenith wet delay; IDW: inverse distance weighting; MC: minimum curvature; IGS: International GNSS service.

Evaluating expressway stability using interferometric synthetic aperture radar and measuring its impact on the occurrence of geohazards: a case study of Shanxi Province, China

ABSTRACT This study designs a coherence-based multi-master-image stacking InSAR workflow as an efficient large-scale expressway stability monitoring method, which can improve the overall coherence and number of interferometric pairs via multi-master-image stacking and an average coherence threshold. The average annual ground deformation rate of Shanxi Province, China, was determined using Sentinel-1A data from 2017 to 2021 with a parallel processing strategy to improve efficiency. Compared to global navigation satellite system data, the InSAR results have a root mean square error of 2.90 mm/year. To evaluate the relationship between different factors and the occurrence of geohazards along the expressway network, we conducted a binary logistic regression analysis. The result shows that the deformation rate, annual precipitation, bedrock hardness, distance to stream, and traffic load are significantly related to the occurrence of geohazards along the expressway network in Shanxi Province, among which the deformation rate has the highest significant value, while the traffic load has the lowest, indicating that using InSAR alone to determine geohazards along expressway network is biased and it takes more than InSAR to conduct a comprehensive yet accurate expressway geohazard vulnerability evaluation. This study provides guidance for the road maintenance to ensure the safe operation of expressway systems.

Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data

In this paper, we examine the use of data from multiple sensing modes, i.e., accelerometry and global navigation satellite system (GNSS), for classifying animal behavior. We extract three new features from the GNSS data, namely, distance from water point, median speed, and median estimated horizontal position error. We combine the information available from the accelerometry and GNSS data via two approaches. The first approach is based on concatenating the features extracted from both sensor data and feeding the concatenated feature vector into a multi-layer perceptron (MLP) classifier. The second approach is based on fusing the posterior probabilities predicted by two MLP classifiers. The input to each classifier is the features extracted from the data of one sensing mode. We evaluate the performance of the developed multimodal animal behavior classification algorithms using two real-world datasets collected via smart cattle collar tags and ear tags. The leave-one-animal-out cross-validation results show that both approaches improve the classification performance appreciably compared with using data of only one sensing mode. This is more notable for the infrequent but important behaviors of walking and drinking. The algorithms developed based on both approaches require little computational and memory resources hence are suitable for implementation on embedded systems of our collar tags and ear tags. However, the multimodal animal behavior classification algorithm based on posterior probability fusion is preferable to the one based on feature concatenation as it delivers better classification accuracy, has less computational and memory complexity, is more robust to sensor data failure, and enjoys better modularity.

A New Approach for Improving GNSS Geodetic Position by Reducing Residual Tropospheric Error (RTE) Based on Surface Meteorological Data

Positioning error components related to tropospheric and ionospheric delays are caused by the atmosphere in positioning determined by global navigation satellite systems (GNSS). Depending on the user’s requirements, the position error caused by tropospheric influences, which is commonly referred to as zenith tropospheric delay (ZTD), must be estimated during position determination or determined later by external tropospheric corrections. In this study, a new approach was adopted based on the reduction of residual tropospheric error (RTE), i.e., the unmodeled part of the tropospheric error that remains included in the total geodetic position error, along with other unmodeled systematic and random errors. The study was performed based on Global Navigation Satellite System (GLONASS) positioning solutions and accompanying meteorological parameters in a defined and harmonized temporal-spatial frame of three locations in the Republic of Croatia. A multidisciplinary approach-based analysis from a navigational science aspect was applied. The residual amount of satellite positioning signal tropospheric delay was quantitatively reduced by employing statistical analysis methods. The result of statistical regression is a model which correlates surface meteorological parameters with RTE. Considering the input data, the model has a regional character, and it is based on the Saastamoinen model of zenith tropospheric delay. The verification results show that the model reduces the RTE and thus increases the geodetic accuracy of the observed GNSS stations (with horizontal components of position accuracy of up to 3.8% and vertical components of position of up to 4.37%, respectively). To obtain these results, the Root Mean Square Error (RMSE) was used as the fundamental parameter for position accuracy evaluation. Although developed based on GLONASS data, the proposed model also shows a considerable degree of success in the verification of geodetic positions based on Global Positioning System (GPS). The purpose of the research, and one of its scientific contributions, is that the proposed method can be used to quantitatively monitor the dynamics of changes in deviations of X, Y, and Z coordinate values along coordinate axes. The results show that there is a distinct interdependence of the dynamics of Y and Z coordinate changes (with almost mirror symmetry), which has not been investigated and published so far. The resultant position of the coordinates is created by deviations of the coordinates along the Y and Z axes—in the vertical plane of space, the deviations of the coordinate X (horizontal plane) are mostly uniform and independent of deviations along the Y and Z axes. The proposed model shows the realized state of the statistical position equilibrium of the selected GNSS stations which were observed using RTE values. Although of regional character, the model is suitable for application in larger areas with similar climatological profiles and for users who do not require a maximum level of geodetic accuracy achieved by using Satellite-Based Augmentation Systems (SBAS) or other more advanced, time-consuming, and equipment-consuming positioning techniques.

An improved stacking filtering for extracting the common-mode errors on GNSS coordinate time series in Shanxi

The presence of common mode error (CME) in the coordinate displacement time series of the Global Navigation Satellite System (GNSS) affects geophysical studies using GNSS observations. In order to investigate the effect of CME on the time series in GNSS networks in Shanxi, this paper proposes an improved superposition filtering method by introducing single-day solution accuracy, correlation coefficient, and spherical distance between stations as weights. The filtering effect is evaluated using the GNSS data in Shanxi. By using the improved stacking filtering method, the root mean square (RMS) values for N, E, U are reduced by approximately 27.8%, 29.0%, and 46.0%, respectively. And compared to the traditional stacking filter, our improved method can achieve better results with CME extraction. We investigate the CME spatial-temporal characteristics and its relationship with environmental loading. The results show that the CME between stations decreases as the distance between stations increases. In addition, we analyze the effect of CME on the noise component and velocity estimates. Results show that removing the CME refines the velocity and leads to a significant reduction in the magnitude of noise, indicating that the CME is dominated by the flicker noise in Shanxi Province.

Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS

Kunming city is located in the middle of Yunnan Province. Due to large-scale groundwater exploitation and urban development in recent years, this area has been affected by surface subsidence. In this paper, Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) data are used to monitor the surface subsidence in Kunming city area for better analysis and understanding. The study used data of Sentinel-1A from 2018 to 2020 with atmospheric correction based on GACOS to calculate the average annual subsidence rate in Kunming city area, and the results show that the maximum subsidence rate is 48 mm/year. The subsidence obtained by InSAR is compared with the vertical deformation information obtained by eight GNSS stations in continuous operation in the study area. The subsidence rate trend show by the two methods is consistent, which further verifies the validity of InSAR data to reflect the local deformation. Experimental results shown that the eastern and northeastern Dianchi lake areas were affected by underground resources mining, and the induced surface subsidence characteristics were obvious, with the surface subsidence rate reachde 48 mm/year and 37 mm/year respectively. The Kunyang Phosphate Mine also had different degrees of mining subsidence disaster, with the maximum subsidence rate reached 36 mm/year. The subsidence rate of InSAR and GNSS has the same trend on the whole. However, GNSS sites are generally located in stable areas, the settlement amount obtained in the same time period is somewhat different from that of InSAR.

Anelastic deformation in the lower crust and its implications for tectonic dynamics in Kyushu, Southwest Japan

Most of the seismicity in inland Kyushu, southwest Japan occur in the localized intraplate shear zones which are Beppu-Shimabara Graben (BSG) and left-lateral shear zone (LSZ). One of the largest fault systems in Japan (Median Tectonic Line—MTL), and subduction zone of the Philippine Sea Plate also exist near Kyushu. These suggest that the island arc, such as Kyushu has a complex deformation process. This study estimated the anelastic strain rate in the lower crust and the interplate coupling rate via inversion analysis using Global Navigation Satellite System data to elucidate the seismotectonics. Deformation in the lower crust and interplate coupling were modeled by cuboid volumetric sources and a back slip model, respectively. To stabilize the solution, a L2 regularization constraint was applied to the anelastic strain rate. Further, constraints on direction and smoothing to the slip deficit rate were applied. These strength of the constraints were determined via the minimum value of the Akaike Bayesian information criterion. The analyses showed that the anelastic strain rate of the BSG and LSZ was ∼0.6 ×10−6 yr−1, and BSG was related to MTL activity. The findings also suggest that this anelastic deformation in the lower crust created a stress concentration zone in the upper crust of BSG and LSZ, with a stress change rate of ∼6–10 kPa·yr−1. The value is consistent with the stress drop and recurrence interval of regional earthquakes, suggesting that the seismicity around BSG and LSZ is strongly influenced by anelastic deformation in the lower crust. It is also suggested that BSG and LSZ development is influenced by slab rollback, and a subducting ridge. Lastly, a comparison between the deviatoric stress field estimated using focal mechanisms and the anelastic strain rate suggests the existence of a shear zone consisting of weak faults south of BSG.

Regional Ionospheric Maps with Quad-Constellation Raw Observations as Applied to Single-Frequency PPP

Ionospheric delay is one of the most problematic errors in single-frequency (SF) global navigation satellite system (GNSS) data processing. Global/regional ionospheric maps (GIM/RIM) are thus vitally important for positioning users. Given the coexistence of multi-GNSS, the integration of quad-constellation observations is essential for improving the distribution of ionospheric penetration points (IPPs) and increasing redundant observations compared with the existing GIM products from the IGS analysis center. In this paper, quad-constellation (GPS/GLONASS/Galileo/BDS) observations are applied to set up the RIM over Australia with uncombined precise point positioning (UC-PPP) and a low-order spherical harmonic function. The generated RIMs are then introduced to ionosphere-corrected (IC) and ionosphere-weighted (IW) single-frequency PPP (IC-SFPPP and IW-SFPPP) to verify their performance in terms of positioning accuracy and convergence time. Taking the CODE GIM as a reference, the results show that the mean root mean square (RMS) of VTEC differences is 0.867 TECUs, and the quad-constellation RIM (referred as ‘RIM4′) can improve the RMS of RIMs compared to single-constellation mode at the edge of regional experiment area. The application of the RIM4 in the BDS IC-SFPPP results in a 18.38% improvement (from 100.47 cm to 82.00 cm) of 3D positioning RMS compared to the CODE-GIMs, whereas 35.36% enhancement (from 115.92 cm to 74.62 cm) of 3D positioning RMS is achievable during an active ionospheric period. Moreover, if the criterion of the convergence time is defined as when positioning errors in the horizontal and vertical directions are less than 0.3 m and 0.6 m for 20 consecutive epochs, the IW-SFPPP can significantly speed up the convergence time compared to the uncombined SFPPP; that is, the convergence time is reduced by 52.7% (from 37 min to 17.5 min), 37.2% (from 72.5 min to 45.5 min), and 37.1% (from 62.0 min to 39.0 min) in the north, east and up direction, respectively, at the 68% confidence level.

Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets

Predicting extreme weather events in a short time period and their developing in localized areas is a challenge. The nowcasting of severe and extreme weather events is an issue for air traffic management and control because it affects aviation safety, and determines delays and diversions. This work is part of a larger study devoted to nowcasting rain and wind speed in the area of Malpensa airport by merging different datasets. We use as reference the weather station of Novara to develop a nowcasting machine learning model which could be reusable in other locations. In this location we have the availability of ground-based weather sensors, a Global Navigation Satellite System (GNSS) receiver, a C-band radar and lightning detectors. Our analysis shows that the Long Short-Term Memory Encoder Decoder (LSTM E/D) approach is well suited for the nowcasting of meteorological variables. The predictions are based on 4 different datasets configurations providing rain and wind speed nowcast for 1 h with a time step of 10 min. The results are very promising with the extreme wind speed probability of detection higher than 90%, the false alarms lower than 2%, and a good performance in extreme rain detection for the first 30 min. The configuration using just weather stations and GNSS data in input provides excellent performances and should be preferred to the other ones, since it refers to the pre-convective environment, and thus can be adaptable to any weather conditions.

Detection and Three‐Dimensional Reconstruction of Concentric Traveling Ionosphere Disturbances Induced by Hurricane Matthew on 7 October 2016

AbstractThis paper reports the first high‐resolution 4‐dimensional ionospheric disturbance caused by hurricane Matthew on 7 October 2016. The temporal as well as vertical and horizontal variations of the concentric traveling ionospheric disturbances (CTIDs) were reconstructed using 3‐dimensional computerized ionospheric tomography (3DCIT) technology, based upon the Global Navigation Satellite System data from the dense receiver network over North America. The frequency range of disturbances was determined by spectrum analysis, and a Butterworth band‐pass filter was used to de‐trend the total electron content (TEC) sequences to determine TIDs. A remarkable CTID segment was detected at a distance of 1,000–1,500 km from the hurricane eye at ∼5:40–6:10 UT on 7 October 2016, moving westward with the horizontal phase velocity of ∼153.4 m/s, the period of ∼30 min and the horizontal wavelength of ∼276.1 km. The positive and negative wavefronts dominated the CITD at different times during the event. From 4:00 to 8:00 UT, the altitudinal variation of the CTIDs in electron density exhibited clear downward phase progression predominately in the range of 150–400 km altitudes; however, the percentage of electron density disturbances was larger below 250 km. The inverted cone‐like geometry of CTID wavefronts was presented. The vertical phase velocities of the CTIDs ∼1,100 km away from the hurricane eye in the northwest direction near 88°W, 34°N were ∼203.7–277.8 m/s, and at the same location, the horizontal phase velocities at 300 km altitude were ∼149.1–181.5 m/s, slightly larger than those at 200 km altitude (∼145.1–178.5 m/s).

Electron density fluctuations from Swarm as a proxy for ground-based scintillation data: A statistical perspective

The Swarm satellite mission has been used for numerous studies of the ionosphere. Here we use a global product, based on electron density measurements from Swarm that characterises ionospheric variability. The IPIR (Ionospheric Plasma IRregularities product) provides characteristics of plasma irregularities in terms of their amplitudes, gradients and spatial scales and assigns them to geomagnetic regions. Ionospheric irregularities and fluctuations are often the cause of errors in position, navigation, and timing (PNT) based on the Global Navigation Satellite Systems (GNSS), in which signals pass through the ionosphere. The IPIR dataset also provides an indication, in the form of a numerical value index (IPIR index), of the severity of irregularities affecting the integrity of trans-ionospheric radio signals and hence, the accuracy of GNSS positioning. We analysed datasets from Swarm A and ground-based scintillation receivers. Time intervals (when Swarm A passes over the field of view of the ground-based GPS receiver) are compared to ground-based scintillation data, collecting an azimuthal selection of the GNSS data relevant to the Swarm satellite overpass. We provide validations of the IPIR product against the ground-based measurements from 23 ground-based receivers, focusing on GPS TEC and scintillation data in low-latitude, auroral and polar regions, and in different longitudinal sectors. We have determined the median, mean, maximum and standard deviation of the parameter values for both datasets and each conjunction point. We found a weak correlation of the intensity of both phase and amplitude scintillation with the IPIR index.

A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning

Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision tropospheric delay augmentation information is vital to speed up the convergence of PPP. In this research, we estimate the zenith tropospheric delay (ZTD) from 2018 to 2019 by static precise point positioning (PPP) using the fixed position mode; GNSS observations were obtained from the National Geomatics Center of China (NGCC). Firstly, ZTD outliers were detected, and data gaps were interpolated using the K-nearest neighbor algorithm (KNN). Secondly, The ZTD differences between the KNN and periodic model were employed as input datasets to train the long short-term memory (LSTM) neural network. Finally, LSTM forecasted ZTD differences and the ZTD periodic signals were combined to recover the final forecasted ZTD results. In addition, the forecasted ZTD results were applied in static PPP as a prior constraint to reduce PPP convergence time. Numerical results show that the average root-mean-square error (RMSE) of predicting ZTD is about 1 cm. The convergence time of the PPP which was corrected by the LSTM-ZTD predictions is reduced by 13.9, 22.6, and 30.7% in the summer, autumn, and winter, respectively, over GPT2-ZTD corrected PPP and unconstrained conventional PPP for different seasons.

The 2021 Greece Central Crete ML 5.8 Earthquake: An Example of Coalescent Fault Segments Reconstructed from InSAR and GNSS Data

The ML 5.8 earthquake that hit the island of Crete on 27 September 2021 is analysed with InSAR (Interferometry from Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) data. The purpose of this work is to create a model with sufficient detail for the geophysical processes that take place in several kilometres below the earth’s surface and improve our ability to observe active tectonic processes using geodetic and seismic data. InSAR coseismic displacements maps show negative values along the LOS of ~18 cm for the ascending orbit and ~20 cm for the descending one. Similarly, the GNSS data of three permanent stations were used in PPK (Post Processing Kinematic) mode to (i) estimate the coseismic shifts, highlighting the same range of values as the InSAR, (ii) model the deformation of the ground associated with the main shock, and (iii) validate InSAR results by combining GNSS and InSAR data. This allowed us to constrain the geometric characteristics of the seismogenic fault and the slip distribution on it. Our model, which stands on a joint inversion of the InSAR and GNSS data, highlights a major rupture surface striking 214°, dipping 50° NW and extending at depth from 2.5 km down to 12 km. The kinematics is almost dip-slip normal (rake −106°), while a maximum slip of ~1.0 m occurred at a depth of ca. 6 km. The crucial though indirect role of inherited tectonic structures affecting the seismogenic crustal volume is also discussed suggesting their influence on the surrounding stress field and their capacity to dynamically merge distinct fault segments.

Glacial isostatic adjustment and post-seismic deformation in Antarctica

Abstract This chapter reviews glacial isostatic adjustment (GIA) and post-seismic deformation in Antarctica. It discusses numerical models and their inputs, and observations and inferences that have been made from them. Both processes are controlled by mantle viscosity but their forcings are different. Ongoing GIA induced by the loss of ice since the last glacial maximum (LGM) could have amounted to 5–15 m of global sea-level rise. However, mantle viscosity is so low in parts of West Antarctica ( c. 10 18 Pa s) that changes in ice thickness over the last centuries and decades have controlled the current uplift rates there. The uplift due to GIA has promoted ice-sheet stability since the LGM, and in West Antarctica GIA is a significant negative feedback on the current decline of the ice sheet. Post-seismic deformation following the 1998 earthquake near the Balleny Islands south of New Zealand has been detected in global navigation satellite system (GNSS) data and compared to model outputs. The best-fitting viscosity for this area is c. 10 19 Pa s, similar to GIA-based estimates for the Antarctic Peninsula. Future work should focus on unifying descriptions of viscosity across geodynamic models, and integrating information from seismic, gravity, experimental and geological data.

An Integrated InSAR and GNSS Approach to Monitor Land Subsidence in the Po River Delta (Italy)

Land subsidence affects many areas of the world, posing a serious threat to human structures and infrastructures. It can be effectively monitored using ground-based and remote sensing techniques, such as the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). GNSS provides high precision measurements, but in a limited number of points, and is time-consuming, while InSAR allows one to obtain a very large number of measurement points, but only in areas characterized by a high and constant reflectivity of the signal. The aim of this work is to propose an approach to combine the two techniques, overcoming the limits of each of them. The approach was applied in the Po River Delta (PRD), an area located in Northern Italy and historically affected by land subsidence. Ground-based GNSS data from three continuous stations (CGNSS) and 46 non-permanent sites (NPS) measured in 2016, 2018, and 2020, and Sentinel-1 and COSMO-SkyMed SAR data acquired from 2016 to 2020, were considered. In the first phase of the method, InSAR processing was calibrated and verified through CGNSS measurements; subsequently, the calibrated interferometric data were used to validate the GNSS measurements of the NPS. In the second phase, the datasets were integrated to provide an efficient monitoring system, extracting high-resolution deformation maps. The results showed a good agreement between the different sources of data, a high correlation between the displacement rate and the age of the emerged surfaces composed of unconsolidated fine sediments, and high land subsidence rates along the coastal area (up to 16–18 mm/year), where the most recent deposits outcrop. The proposed approach makes it possible to overcome the disadvantages of each technique by providing more complete and detailed information for a better understanding of the ongoing phenomenon.

BDS and Galileo: Global Ionosphere Modeling and the Comparison to GPS and GLONASS

The ionospheric delay is one of the important error sources in the Global Navigation Satellite System (GNSS) data processing. With the rapid construction and development of GNSS, the abundant satellite resources have brought new opportunities for ionospheric monitoring. To further investigate the performances and abilities of Galileo and BDS in ionosphere modeling, we study the ionosphere modeling based on the 15th order spherical harmonic function, and 364 stations around the world are selected for global ionospheric modeling of GPS, GLONASS, Galileo and BDS systems under ionospheric quiet and active conditions, respectively. The results show that the average biases of the ionospheric models built by GPS, GLONASS and Galileo are relatively small, which are within 2 Total Electron Content Unit (TECU) as compared to the Center for Orbit Determination in Europe (CODE) global ionospheric map (GIM), while the average biases of the models built by BDS are between 6 and 8 TECU during the ionospheric quiet and active days, respectively. In addition, in order to analyze the modeling performances before and after using BDS geostationary earth orbit (GEO) satellites, BDS is divided into two groups, in which one group contains medium earth orbit (MEO), inclined geosynchronous orbit (IGSO) and GEO satellites; and the other group contains only MEO and IGSO satellites. The results show that the influence of GEO satellites on ionospheric modeling is less than 1 TECU. Due to the distribution of the stations, the 0-value region in the ionospheric model is mainly distributed in the mid and high-latitude regions of the southern hemisphere. Since the ionospheric parameters are lumped with the Differential Code Bias (DCB), we also estimate the DCB parameters and analyze their performances. The DCB estimated in ionosphere modeling shows strong stability, with the average biases of GPS, GLONASS, Galileo and BDS under 0.25 ns, 0.25 ns, 0.2 ns and 0.42 ns, respectively. We also estimate other DCB types of the four GNSS systems. The results show that the DCB is stable and shows consistency with Chinese Academy of Sciences (CAS) DCB products.

An optimized hydrological drought index integrating GNSS displacement and satellite gravimetry data

Space geodetic techniques are rapidly emerging as powerful tools for drought monitoring, including the Global Navigation Satellite System (GNSS) positioning data, and the gravimetric data from Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. However, while GNSS-based 3D displacements have high temporal resolution, they are impacted by diverse local effects especially for tectonically active regions. Satellite gravimetry derived terrestrial water storage changes (ΔTWS) are continuous in temporal sampling, however, at much coarser spatial resolutions, and there are systematic noise and a large 11-month data gap between GRACE and GRACE-FO observations. Here for the first time, we developed an Optimized GNSS/GRACE/GRACE-FO Hydrological Drought Index (OGHDI), by optimally combining two drought indices derived from GNSS vertical displacements (VDs) and GRACE/GRACE-FO ΔTWS, to better characterize decadal evolution of droughts over Southwest China, 2011–2020. The optimal relative weights of the two drought indices are determined through maximizing the correlations with the Composite Index (CI) commonly used in the study region. Then OGHDI is compared to the self-calibrating Palmer Drought Severity Index (scPDSI). The results show that VDs agree well with ΔTWS at most of the 30 analyzed GNSS sites, with a mean correlation of −0.44. Compared to the drought index developed using only GNSS data, the optimized technique improved the OGHDI-scPDSI correlations at 90% (27 sites) of the sites, with the highest correlation reaches 0.73. Further, more significant improvement is found at the regional scale than at individual sites, and the regional mean OGHDI-CI and OGHDI-scPDSI correlations increase from 0.37 to 0.57 and from 0.42 to 0.68, respectively. All drought indices show that Southwest China experienced two sustained and excessive drought periods from 2011 to mid-2013, and after 2019. We conclude that the novel integration of two distinct contemporary and complemenatry geodetic datasets, GNSS and GRACE/GRACE-FO, improved the ability of drought monitoring in Southwest China.