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Advances in GNSS for Time Series Analysis

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 May 2025) | Viewed by 11733

Special Issue Editors


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Guest Editor
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Interests: GNSS data analysis including POD; clock analysis; SBAS; PPP; PPP-RTK; LEO augmentation; reference frame and Geodynamics

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Guest Editor
Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38122 Trento, Italy
Interests: methods for spatial and temporal filtering of remote sensed data and digital surface models; GNSS for water resources; SAR and optical remote sensing for river and lake science
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Guest Editor
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Interests: tectonics; geodynamics; crustal deformation mechanism; GNSS timeseries analysis

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Guest Editor
The First Monitoring and Application Center, China Earthquake Administration, China
Interests: crustal deformation characteristics; InSAR precision data processing; geodetic data fusion method; 3D crustal deformation field

Special Issue Information

Dear Colleagues,

Recently, GNSS has preliminarily determined the basic characteristics of continental crustal movements. GNSS station coordinates time series which usually contains linear and nonlinear signals caused by tectonic movements, annual variations caused by mass loads, co-seismic and post-seismic deformation, the inflation and eruption of active volcanoes, glacial-isostatic adjustment and its effects on relative sea level, as well as uneliminated systematic errors in GNSS data processing. However, real-time earth deformation monitoring at different temporal and spatial scales requires higher GNSS precision. How to distinguish the above signals from other geophysical processes, especially for transient and weak signals related to tectonic deformation, and obtain high precision crustal movement information, is an urgent problem in the current coordinate time series analysis.

This Special Issue aims to cover different uses of GNSS in geophysics and geodesy. Topics may cover anything from GNSS time series analysis methods, signals observed by GNSS, noise analysis in GNSS timeseries, etc. Hence, multisource data integration, multiscale approaches, or studies focused on GNSS timeseries analysis, among other issues, are welcome. Articles may address, but are not limited to, the following topics:

  • GNSS coordinate timeseries analysis;
  • Detection and analysis of signals derived from geodetic time series data;
  • Active crustal deformation processes observed by GNSS;
  • Multivariate statistical techniques application on GNSS;
  • Noise in GPS position time-series.

Prof. Dr. Junping Chen
Dr. Alfonso Vitti
Dr. Weijie Tan
Dr. Nannan Guo
Guest Editors

Manuscript Submission Information

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Keywords

  • non-linear crustal deformation
  • common mode error
  • independent component analysis
  • noise analysis
  • mass loadings

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Published Papers (7 papers)

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Research

18 pages, 14698 KiB  
Article
Analysis on GNSS Common View and Precise Point Positioning Time Transfer: BDS-3/Galileo/GPS
by Meng Wang, Chunlei Pang, Dong Guo, Shize Wang, Yang Zhang, Jinglong Gao and Xiubin Zhao
Remote Sens. 2025, 17(10), 1725; https://doi.org/10.3390/rs17101725 - 15 May 2025
Viewed by 137
Abstract
The International Bureau of Weights and Measures (BIPM) currently mainly uses GPS time transfer for the calculation of UTC. In order to enhance the reliability of the time links, the common-view (CV) and Precise Point Positioning (PPP) time transfer performance of the dual-frequency [...] Read more.
The International Bureau of Weights and Measures (BIPM) currently mainly uses GPS time transfer for the calculation of UTC. In order to enhance the reliability of the time links, the common-view (CV) and Precise Point Positioning (PPP) time transfer performance of the dual-frequency ionosphere-free combination for BRUX-SPT0, NIST-USN7, and BRUX-USN7 links was evaluated, including GPS (P1 & P2), Galileo (E1 & E5a), and BDS-3 (B1I & B3I, B1I & B2a, B1C & B3I, B1C & B2a). The experimental results show that the precision and average frequency stability (AFT) of BDS-3 B1C & B2a CV and PPP links are better than those of BDS-3 B1I & B3I, B1I & B2a, and B1C & B3I links. Compared to the GPS P1 & P2 and BDS-3 B1C & B2a CV links, the Galileo E1 & E5a links have the highest precision. In addition, the precision of GPS PPP links outperforms the BDS-3 and Galileo links. The short-term FT (frequency stability) of GPS PPP links is better than that of BDS-3 B1C & B2a PPP links. When the average time is greater than 4.3 h, however, the BDS-3 B1C & B2a PPP link’s AFT is significantly improved compared with the Galileo PPP links. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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19 pages, 5486 KiB  
Article
Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
by Cong Shen, Guocheng Wang, Lintao Liu, Dong Ren, Huiwen Hu and Wenlong Sun
Remote Sens. 2025, 17(5), 827; https://doi.org/10.3390/rs17050827 - 27 Feb 2025
Viewed by 458
Abstract
Effective noise management and control of periodic fluctuations in spaceborne atomic clocks are essential for the accuracy and reliability of Global Navigation Satellite Systems. Time-varying periodic terms can impact both the performance evaluation and prediction accuracy of satellite clocks, making it crucial to [...] Read more.
Effective noise management and control of periodic fluctuations in spaceborne atomic clocks are essential for the accuracy and reliability of Global Navigation Satellite Systems. Time-varying periodic terms can impact both the performance evaluation and prediction accuracy of satellite clocks, making it crucial to mitigate these influences in the clock bias. We propose methods based on the Fourier dictionary and basis pursuit, namely the Fourier basis pursuit (FBP) spectrum and the Fourier basis pursuit bandpass filter (FBPBPF), to analyze and extract periodic terms in the satellite clock bias. The FBP method minimizes the L1-norm to improve spectral quality, while the FBPBPF reduces boundary effects and noise. Our experimental results show that the FBP spectrum has a more obvious main lobe and reduces spectral leakage compared to traditional windowed Fourier transforms. In simulation experiments, the FBPBPF achieves periodic term extraction with errors reduced by 6.81% to 26.55% compared to traditional signal processing methods, and boundary extraction errors reduced by up to 63.67%. Using the BeiDou Navigation Satellite System’s precise clock bias for verification, the FBP-based prediction method has significantly improved the prediction accuracy compared to the spectral analysis model. For 6, 12, 18, and 24 h predictions, the average root mean square error of the FBP prediction method is reduced by 15.85%, 11.04%, 6.45%, and 4.01%, respectively. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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18 pages, 8017 KiB  
Article
Regional GNSS Common Mode Error Correction to Refine the Global Reference Frame
by Ruyuan Wang, Junping Chen, Danan Dong, Weijie Tan and Xinhao Liao
Remote Sens. 2024, 16(23), 4469; https://doi.org/10.3390/rs16234469 - 28 Nov 2024
Cited by 1 | Viewed by 1062
Abstract
Common mode error (CME) arises from various sources, including unknown regional errors, potential geophysical signals, and other factors present in global navigation satellite system (GNSS) coordinate solutions, undeniably affecting the GNSS precision. This research concentrates on the effects of CME correction in global [...] Read more.
Common mode error (CME) arises from various sources, including unknown regional errors, potential geophysical signals, and other factors present in global navigation satellite system (GNSS) coordinate solutions, undeniably affecting the GNSS precision. This research concentrates on the effects of CME correction in global IGS-based reference frame refinement. We first estimated the regional CME with principal component analysis to obtain CME-corrected GNSS coordinate solutions. Subsequently, effects on the global reference frame with the regional CME correction were analyzed in three aspects: accuracy improvement of the coordinate solutions, variation in the velocity field, and accuracy improvement of the Helmert parameters in the reference frame transformation. The results show that after applying CME correction, the GNSS coordinate accuracy was improved by 28.9%, 22.1%, and 29.5% for the east, north, and vertical components, respectively. Regarding the site velocities, the maximum difference in velocity reached 0.48 mm/yr. In addition, the standard deviation of the Helmert transformation parameters between the International Terrestrial Reference Frame (ITRF) and the IGS-based reference frame—exclusively derived from GNSS technology—was reduced by over 30%, indicating CME correction enhanced the accuracy of the transformation parameters and refined the IGS-based reference frame. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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19 pages, 2958 KiB  
Article
On the Consistency of Stochastic Noise Properties and Velocity Estimations from Different Analysis Strategies and Centers with Environmental Loading and CME Corrections
by Hongli Lv, Xiaoxing He, Shunqiang Hu, Xiwen Sun, Jiahui Huang, Rui Fernandes, Wen Xie and Huajiang Xiong
Remote Sens. 2024, 16(18), 3518; https://doi.org/10.3390/rs16183518 - 22 Sep 2024
Viewed by 1043
Abstract
The analysis of the Global Navigation Satellite System (GNSS) time series provides valuable information for geodesy and geodynamics research. Precise data analysis strategies are crucial for accurately obtaining the linear velocity of GNSS stations, enabling high-precision applications of GNSS time series. This study [...] Read more.
The analysis of the Global Navigation Satellite System (GNSS) time series provides valuable information for geodesy and geodynamics research. Precise data analysis strategies are crucial for accurately obtaining the linear velocity of GNSS stations, enabling high-precision applications of GNSS time series. This study investigates the impact of different stochastic noise models on velocity estimations derived from GNSS time series, specifically under conditions of environmental loading correction and common mode error (CME) removal. By comparing data from various data centers, we find that post-correction, different analysis strategies exhibit high consistency in their noise characteristics and velocity estimation results. Across various analysis strategies, the optimal noise models were predominantly Power Law with White Noise (PLWN) and Flicker Noise with White Noise (FNWN), with the optimal noise models including COMB/JPL, COMB/SOPAC, and COMB/NGL for approximately 50% of the datasets. Most of the stations (approximately 80%) showed velocity differences below 0.3 mm/year and velocity estimation uncertainties below 0.1 mm/year. Nonetheless, variations in amplitudes and periodic signals persisted due to differences in the processing of raw GNSS observations. For instance, the NGL and JPL datasets, which were processed using GipsyX 2.1 software, showed higher amplitudes of the 5.5-day periodic signal. These findings provide a solid empirical foundation for advancing data analysis methods and enhancing the reliability of GNSS time series results in future research. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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21 pages, 9404 KiB  
Article
Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model
by Yilin Xie, Xiaolin Meng, Jun Wang, Haiyang Li, Xun Lu, Jinfeng Ding, Yushan Jia and Yin Yang
Remote Sens. 2024, 16(10), 1767; https://doi.org/10.3390/rs16101767 - 16 May 2024
Cited by 4 | Viewed by 1788
Abstract
Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory [...] Read more.
Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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17 pages, 6594 KiB  
Article
Python Software Tool for Diagnostics of the Global Navigation Satellite System Station (PS-NETM)–Reviewing the New Global Navigation Satellite System Time Series Analysis Tool
by Stepan Savchuk, Petro Dvulit, Vladyslav Kerker, Daniel Michalski and Anna Michalska
Remote Sens. 2024, 16(5), 757; https://doi.org/10.3390/rs16050757 - 21 Feb 2024
Cited by 1 | Viewed by 1458
Abstract
The time series of GNSS coordinates contain signals caused by the age-related movement of tectonic plates, the deformation of the Earth’s surface, as well as errors at different time scales from sub-daily tidal deformation to the long-term deformation of the surface load. Depending [...] Read more.
The time series of GNSS coordinates contain signals caused by the age-related movement of tectonic plates, the deformation of the Earth’s surface, as well as errors at different time scales from sub-daily tidal deformation to the long-term deformation of the surface load. Depending on the nature of the signal, specific approaches are used for both the visual interpretation and pre-processing of time series and their statistical analysis. However, none of the present software analyzes the nature of the residual errors but assumes their random nature and obedience to the classical normal distribution. One of the methods for analyzing the time series of coordinates with residual, unaccounted-for systematic errors is the non-classical error theory of measurements. The result of this work is a developed software solution for analyzing the time series of GNSS coordinates to test their normality, or in other words, to test whether a particular GNSS station is subject to the influence of small, unaccounted-for errors. Conclusions: After testing our software on four reference stations in Europe, we concluded that none of the chosen stations followed the normal law of distribution; thus, it is vital to perform such tests before conducting any experiments on the time series from reference stations. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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22 pages, 7826 KiB  
Article
An Improved VMD-LSTM Model for Time-Varying GNSS Time Series Prediction with Temporally Correlated Noise
by Hongkang Chen, Tieding Lu, Jiahui Huang, Xiaoxing He, Kegen Yu, Xiwen Sun, Xiaping Ma and Zhengkai Huang
Remote Sens. 2023, 15(14), 3694; https://doi.org/10.3390/rs15143694 - 24 Jul 2023
Cited by 23 | Viewed by 4497
Abstract
GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and the maintenance of the global coordinate framework. Long short-term memory (LSTM) is a deep learning model that has been widely applied in the field of high-precision time [...] Read more.
GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and the maintenance of the global coordinate framework. Long short-term memory (LSTM) is a deep learning model that has been widely applied in the field of high-precision time series prediction and is often combined with Variational Mode Decomposition (VMD) to form the VMD-LSTM hybrid model. To further improve the prediction accuracy of the VMD-LSTM model, this paper proposes a dual variational modal decomposition long short-term memory (DVMD-LSTM) model to effectively handle noise in GNSS time series prediction. This model extracts fluctuation features from the residual terms obtained after VMD decomposition to reduce the prediction errors associated with residual terms in the VMD-LSTM model. Daily E, N, and U coordinate data recorded at multiple GNSS stations between 2000 and 2022 were used to validate the performance of the proposed DVMD-LSTM model. The experimental results demonstrate that, compared to the VMD-LSTM model, the DVMD-LSTM model achieves significant improvements in prediction performance across all measurement stations. The average RMSE is reduced by 9.86% and the average MAE is reduced by 9.44%; moreover, the average R2 increased by 17.97%. Furthermore, the average accuracy of the optimal noise model for the predicted results is improved by 36.50%, and the average velocity accuracy of the predicted results is enhanced by 33.02%. These findings collectively attest to the superior predictive capabilities of the DVMD-LSTM model, thereby demonstrating the reliability of the predicted results. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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