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Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

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

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 46876

Special Issue Editors

Dr. Xiaoxing He

E-Mail Website
Guest Editor
School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Interests: high-precision navigation and positioning; sea level change; big data analysis; remote sensing environment monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Jean-Philippe Montillet

E-Mail Website
Guest Editor
Physikalisch - Meteorologisches Observatorium Davos/World Radiation Center, 7260 Davos Dorf, Switzerland
Interests: satellite geodesy; stochastic model analysis; sea level
Prof. Dr. Zhao Li

E-Mail Website
Guest Editor
GNSS Research Centre, Wuhan University, Wuhan 430079, China
Interests: satellite geodesy; satellite altimetry; environmental loading modeling; GNSS data processing; time series analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Gaël Kermarrec

E-Mail Website
Guest Editor
Geodetic Institute, Leibniz Universität Hannover, Nienburger Str. 1, 30167 Hannover, Germany
Interests: stochastic modelling; turbulence theory; surface fitting with splines; terrestrial laser scanner; global navigation satellite system (GNSS)
Dr. Rui Fernandes

E-Mail Website1 Website2
Guest Editor
Department of Computer Sciences, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
Interests: accurate positioning; computer sciences; geodynamics; GNSS; plate tectonics
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Zhou

E-Mail Website1 Website2
Guest Editor
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Interests: multi-GNSS PPP-RTK; real-time atmospheric modeling; multi-sensor integration and data processing; GNSS software tool development

Special Issue Information

Dear Colleagues,              

Modern geodesy aims to accurately measure and understand the topography of the Earth and its gravitational field. Exemplarily, time series of coordinates recorded with highly accurate GNSS receivers provide important information about plate tectonics, volcano deflation/inflation events, post-glacial rebound, glaciers, or earthquakes. Furthermore, one can detect particular transient signals within these observations, e.g., slow slip events and post-seismic transients. The latter are precursors of natural hazards (earthquakes, landslides, flooding, etc.) and need to be accurately modelled. Regarding climate changes and/or the increase of natural phenomena with potentially dramatic consequences on populations, studying geodynamics and geophysics processes with various geodetic time series will provide theoretical and technical support for the establishment and maintenance of global and regional reference frames, earthquake disaster monitoring, and other engineering and scientific research.

This Special Issue focuses on modelling the geodetic time series recorded by various instruments to monitor the aforementioned phenomena using modern technologies (e.g., GNSS, GRACE, InSAR, Terrestrial Laser Scanners (TLS)). We emphasize the recent advances in the detection of small amplitude transient signals, periodic signals, and long-term trends (e.g., seasonal signals, tectonic rate, etc.) that are contaminated by various types of noise (i.e., stochastic processes, correlations). We especially welcome contributions focusing on modern algorithms and statistical estimators (e.g., MLE, wavelet, machine learning, and data fusion) highlighting the recent progress in statistics applied to environmental geodesy.

Potential topics include, but are not limited to, the following:

  1. Geodetic Time Series Analysis: Stochastic Noise Modelling, Functional Model Fitting, Transient Signal Detection, Spatiotemporal Filtering.
  2. Application of Machine Learning on Geodetic Time Series Analysis.
  3. Tectonic Activity Inferred from GNSS Velocity Field.
  4. Crustal Deformation Pattern Detection by Integration of GNSS, TLS, Tide Gauge and InSAR Techniques, Including Stochastic Modelling.
  5. Seasonal Hydrological/Environment Loading with GNSS, GRACE, and Hydrological Model.
  6. Dynamic Sea Level Changes and Coastal Disaster Early Warning Based on Earth Observations (GNSS, Tide Gauge, SSH).
  7. Strong Convective Weather (GNSS-Derived Troposphere Variation Characteristics).

Dr. Xiaoxing He
Dr. Jean-Philippe Montillet
Dr. Zhao Li
Dr. Gaël Kermarrec
Dr. Rui Fernandes
Dr. Feng Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GNSS
  • Geodesy
  • InSAR
  • Geodetic Time Series Analysis
  • Stochastic Noise Modelling
  • Machine Learning
  • Transient Signal Detection
  • Hydrological/Environment Loading Modeling
  • Sea Level Changes

Published Papers (23 papers)

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Editorial

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11 pages, 3946 KiB  
Editorial
Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring
by Xiaoxing He, Jean-Philippe Montillet, Zhao Li, Gaël Kermarrec, Rui Fernandes and Feng Zhou
Remote Sens. 2022, 14(23), 6164; https://doi.org/10.3390/rs14236164 - 05 Dec 2022
Cited by 1 | Viewed by 2172
Abstract
Geodesy is the science of accurately measuring the topography of the earth (geometric shape and size), its orientation in space, and its gravity field. With the advances in our knowledge and technology, this scientific field has extended to the understanding of geodynamical phenomena [...] Read more.
Geodesy is the science of accurately measuring the topography of the earth (geometric shape and size), its orientation in space, and its gravity field. With the advances in our knowledge and technology, this scientific field has extended to the understanding of geodynamical phenomena such as crustal motion, tides, and polar motion. This Special Issue is dedicated to the recent advances in modelling geodetic time series recorded using various instruments. Due to the stochastic noise properties inherent in each of the time series, careful modelling is necessary in order to extract accurate geophysical information with realistic associated uncertainties (statistically sufficient). The analyzed data have been recorded with various space missions or ground-based instruments. It is impossible to be comprehensive in the vast and dynamic field that is Geodesy, particularly so-called “Environmental Geodesy”, which intends to understand the Earth’s geodynamics by monitoring any changes in our environment. This field has gained much attention in the past two decades due to the need by the international community to understand how climate change modifies our environment. Therefore, this Special Issue collects some articles which emphasize the recent development of specific algorithms or methodologies to study particular natural phenomena related to the geodynamics of the earth’s crust and climate change. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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Research

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27 pages, 5839 KiB  
Article
Sea Level Rise Estimation on the Pacific Coast from Southern California to Vancouver Island
by Xiaoxing He, Jean-Philippe Montillet, Rui Fernandes, Timothy I. Melbourne, Weiping Jiang and Zhengkai Huang
Remote Sens. 2022, 14(17), 4339; https://doi.org/10.3390/rs14174339 - 01 Sep 2022
Cited by 7 | Viewed by 1511
Abstract
Previous studies have estimated the sea level rise (SLR) at various locations on the west coast of the USA and Vancouver Island in Canada. Here, we construct an entire SLR profile from Vancouver Island in the Pacific Northwest to San Diego in Southern [...] Read more.
Previous studies have estimated the sea level rise (SLR) at various locations on the west coast of the USA and Vancouver Island in Canada. Here, we construct an entire SLR profile from Vancouver Island in the Pacific Northwest to San Diego in Southern California. First, we process global navigation satellite system (GNSS) measurements at 405 stations blanketing the whole coast to generate a profile of vertical land motion (VLM) known to bias century-long tide gauge (TG) measurements recording relative SLR (RSLR). We are then able to estimate the absolute SLR (ASLR) by correcting the SLR with the VLM. Our study emphasizes the relationship between the various tectonic movements (i.e., the Cascadia subduction zone, the San Andreas strike-slip fault system) along the Pacific coast which renders it difficult to accurately estimate the SLR. That is why we precisely model the stochastic noise of both GNSS and tide gauge time series using a combination of various models and information criterions (ICs). We also use the latest altimetry products and sea surface height (SSH) to compare it with ASLR at the same location as the TGs. This study supports previous analysis that the power law + white noise and generalized Gauss–Markov + white noise models are the best stochastic noise models for the GNSS time series. The new coastal profile confirms the large variability of VLM estimates in the Pacific Northwest around the Cascadia subduction zone in agreement with previous studies, and a similar result when the San Andreas fault comes onshore in Central California (San Francisco Bay). Negative RSLR values are mostly located in the Pacific Northwest (Vancouver Island and Olympic Peninsula). We also observe a much bigger variation (about 90–150%) of the ASLR in the Pacific Northwest which is predominantly due to glacial isostatic adjustment (GIA). Moreover, the comparison between the ASLR and the SSH estimates shows similarities in the center of the studied area (South Washington, Oregon planes, and some parts of Southern California) where the tectonic activity does not significantly influence the TG measurements. Finally, the twentieth-century satellite geocentric ocean height rates show a global mean of 1.5 to 1.9 mm/yr. Our estimates based on ASLR and SSH are within this interval. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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19 pages, 10787 KiB  
Article
Contribution of GRACE Satellite Mission to the Determination of Orthometric/Normal Heights Corrected for Their Dynamics—A Case Study of Poland
by Malgorzata Szelachowska, Walyeldeen Godah and Jan Krynski
Remote Sens. 2022, 14(17), 4271; https://doi.org/10.3390/rs14174271 - 30 Aug 2022
Cited by 6 | Viewed by 1613
Abstract
Physical heights were traditionally determined without considering the dynamic processes of the Earth induced from temporal mass variations. The Gravity Recovery and Climate Experiment (GRACE) mission provided valuable data that allow the estimation of geoid/quasigeoid height changes and vertical deformations of the Earth’s [...] Read more.
Physical heights were traditionally determined without considering the dynamic processes of the Earth induced from temporal mass variations. The Gravity Recovery and Climate Experiment (GRACE) mission provided valuable data that allow the estimation of geoid/quasigeoid height changes and vertical deformations of the Earth’s surface induced from temporal mass loading, and thereby temporal variations of physical heights. The objective of this investigation is to discuss the determination of orthometric/normal heights considering mass transports within the Earth’s system. An approach to determine such heights was proposed. First, temporal variations of orthometric/normal heights (ΔH/ΔH*) were determined using the release 6 GRACE-based Global Geopotential Models together with load Love numbers obtained from the preliminary reference Earth model. Then, those variations were modelled and predicted using the seasonal decomposition (SD) method. The proposed approach was tested on the territory of Poland. The main results obtained reveal that ΔH/ΔH* over the area investigated are at the level of a couple of centimetres and that they can be modelled and predicted with a millimetre accuracy using the SD method. Orthometric/normal heights corrected for their dynamics can be determined by combining modelled ΔH/ΔH* with orthometric/normal heights referred to a specific reference epoch. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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21 pages, 24116 KiB  
Article
CAiTST: Conv-Attentional Image Time Sequence Transformer for Ionospheric TEC Maps Forecast
by Guozhen Xia, Moran Liu, Fubin Zhang and Chen Zhou
Remote Sens. 2022, 14(17), 4223; https://doi.org/10.3390/rs14174223 - 27 Aug 2022
Cited by 13 | Viewed by 1859
Abstract
In recent years, transformer has been widely used in natural language processing (NLP) and computer vision (CV). Comparatively, forecasting image time sequences using transformer has received less attention. In this paper, we propose the conv-attentional image time sequence transformer (CAiTST), a transformer-based image [...] Read more.
In recent years, transformer has been widely used in natural language processing (NLP) and computer vision (CV). Comparatively, forecasting image time sequences using transformer has received less attention. In this paper, we propose the conv-attentional image time sequence transformer (CAiTST), a transformer-based image time sequences prediction model equipped with convolutional networks and an attentional mechanism. Specifically, we employ CAiTST to forecast the International GNSS Service (IGS) global total electron content (TEC) maps. The IGS TEC maps from 2005 to 2017 (except 2014) are divided into the training dataset (90% of total) and validation dataset (10% of total), and TEC maps in 2014 (high solar activity year) and 2018 (low solar activity year) are used to test the performance of CAiTST. The input of CAiTST is presented as one day’s 12 TEC maps (time resolution is 2 h), and the output is the next day’s 12 TEC maps. We compare the results of CAiTST with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The root mean square errors (RMSEs) from CAiTST with respect to the IGS TEC maps are 4.29 and 1.41 TECU in 2014 and 2018, respectively, while the RMSEs of the 1-day CODE prediction model are 4.71 and 1.57 TECU. The results illustrate CAiTST performs better than the 1-day CODE prediction model both in high and low solar activity years. The CAiTST model has less accuracy in the equatorial ionization anomaly (EIA) region but can roughly predict the features and locations of EIA. Additionally, due to the input only including past TEC maps, CAiTST performs poorly during magnetic storms. Our study shows that the transformer model and its unique attention mechanism are very suitable for images of a time sequence forecast, such as the prediction of ionospheric TEC map sequences. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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17 pages, 3826 KiB  
Article
Analysis of Annual Deformation Characteristics of Xilongchi Dam Using Historical GPS Observations
by Ruijie Xi, Yuhan Liang, Qusen Chen, Weiping Jiang, Yan Chen and Simin Liu
Remote Sens. 2022, 14(16), 4018; https://doi.org/10.3390/rs14164018 - 18 Aug 2022
Cited by 6 | Viewed by 1172
Abstract
Global Positioning System (GPS) has been confirmed to be a feasible tool to measure displacement of civil engineering structures. In this paper, we report on an analysis of annual deformations of a pumped-storage power station dam using historical GPS observations. Data spanning more [...] Read more.
Global Positioning System (GPS) has been confirmed to be a feasible tool to measure displacement of civil engineering structures. In this paper, we report on an analysis of annual deformations of a pumped-storage power station dam using historical GPS observations. Data spanning more than nine years are resolved using the GAMIT (GPS at MIT) software, and a GPS time-series method is employed to extract linear trends and annual cycle signals. It is evident that the monument located on the main dam has a linear trend, with rates of 1.0 mm/yr and 1.8 mm/yr in east and up directions, respectively. Annual cycles with amplitudes larger than 0.5 mm are shown in coordinate components at all monitoring stations. However, the annual amplitude can be 30–84% lower when a monitoring station whose monument materials and height are similar to other monitoring stations is chosen as the reference station. This suggests that differential thermal expansion of monuments could be 30% to 80% and even higher in baseline time series. A spurious offset style annual signal with 5 mm amplitude that is highly correlated with annual temperature variance is observed in the east–west direction of the monitoring station located at the east side of the reservoir. This suggests that upper ground layer movement correlated with temperature could be responsible for these annual cycles. Meanwhile, no periodic correlations are observed between the water level data and the baseline time series. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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15 pages, 5213 KiB  
Article
Stable Regional Reference Frame for Reclaimed Land Subsidence Study in East China
by Yu Peng, Danan Dong, Wen Chen and Chenglong Zhang
Remote Sens. 2022, 14(16), 3984; https://doi.org/10.3390/rs14163984 - 16 Aug 2022
Cited by 5 | Viewed by 1182
Abstract
This study implemented a stable Regional Reference Frame in Shanghai, East China (called SHRRF), using seven years of continuous GNSS observations from the Shanghai Continuously Operating Reference System stations (SHCORS) to examine reclaimed coast–land subsidence. A well−distributed core station network suitable for regional [...] Read more.
This study implemented a stable Regional Reference Frame in Shanghai, East China (called SHRRF), using seven years of continuous GNSS observations from the Shanghai Continuously Operating Reference System stations (SHCORS) to examine reclaimed coast–land subsidence. A well−distributed core station network suitable for regional applications was derived. The instantaneous station coordinates and seven frame parameters (translations, rotations, and scale) were estimated at each epoch through minimum constraint during the process of aligning SHRRF to the International Terrestrial Reference Frame (ITRF14). The average root mean square error (RMSE) of all stations under SHRRF was within 1.5 mm horizontally and 5 mm vertically for most epochs. Simultaneously, compared with the ITRF14 solutions, the average RMSE for each site at all epochs was reduced by ~30% horizontally and ~10% vertically. A temporal consolidation settlement model of the reclaimed soil under self−weight was established by combining a geotechnical−derived model with high precision permanent GNSS vertical solutions under SHRRF. The model indicates that ~50% of settlements occurred within 3.6 years, with the whole subsidence time being 46 years. SHRRF provides a precise regional reference frame for use in many East China geophysical applications besides reclaimed coast–land settlement including hydrologic loading, microplate motions, and critical structure deformation monitoring. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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23 pages, 73053 KiB  
Article
Two-Decade GNSS Observation Processing and Analysis with the New IGS Repro3 Criteria: Implications for the Refinement of Velocity Field and Deformation Field in Continental China
by Hu Wang, Yingying Ren, Ahao Wang, Jiexian Wang, Yingyan Cheng, Shushan Fang and Qiang Yang
Remote Sens. 2022, 14(15), 3719; https://doi.org/10.3390/rs14153719 - 03 Aug 2022
Cited by 3 | Viewed by 1442
Abstract
Extensive observation collection, unified and rigorous data processing, and accurate construction of the station motion model are the three essential elements for the accuracy and reliability of the Global Navigation Satellite System (GNSS) velocity field. GNSS data reprocessing not only can weaken the [...] Read more.
Extensive observation collection, unified and rigorous data processing, and accurate construction of the station motion model are the three essential elements for the accuracy and reliability of the Global Navigation Satellite System (GNSS) velocity field. GNSS data reprocessing not only can weaken the influence of untrue nonlinear site signals caused by imperfect models but also can eliminate the displacement offset caused by frame transformation, solution strategy, and model change. Based on the new repro3 criteria of the International GNSS Service (IGS), we process rigorously GNSS observations of continental China from the period 2000 to 2020 to refine GNSS station secular velocities and analyze the present-day crustal deformation in continental China. The main contributions of this work included the followings. Firstly, the repro3 algorithm and model are used to uniformly and rigorously process the two-decade GNSS historical observations to obtain more reliable GNSS coordinate time series with mm-level precision. Combined with the historical records of major earthquakes in continental China, we build a GNSS time series model considering nonlinear factors (velocity, offset, period, co-seismic/post-seismic deformation) to extract GNSS horizontal velocity field whose root mean square (RMS) mean is 0.1 mm/a. Secondly, the GNSS horizontal grid velocity field in continental China is interpolated using the gpsgridder method (the minimum radius is set to 16, and the Poisson’s ratio is set to 0.5). Estimation and analysis of the crustal strain rate solution lead to the conclusion that the strain degree in West China (the high strain region is mainly located in the Qinghai Tibet Plateau and Tianshan Mountains) is much more intense than that in the east (the main strain rate is less than 5 nstrain/year). In addition, most strong earthquakes in the Chinese mainland occurred on active blocks and their boundary faults with large changes in the GNSS velocity field and strain field. Then, an improved K-means++ clustering analysis method is proposed to divide active blocks using GNSS horizontal velocity field. Furthermore, different relative motion models of different blocks are constructed using the block division results. Among them, the Eurasian block has the lowest accuracy (the RMS of residual velocity in the east and north directions are 5.60 and 9.65 mm/a, respectively), and the China block 7 has the highest accuracy (the RMS mean of relative velocity in the east and north directions are 2.60 and 2.65 mm/a, respectively). More observations (2260+ sites), longer time (20 years), and updated criteria (Repro3) are to finely obtain the GNSS velocity field in continental China, and depict crustal deformation and active block with the gpsgridder and improved K-means++ methods. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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17 pages, 3978 KiB  
Article
Excitations of Seasonal Polar Motions Derived from Satellite Gravimetry and General Circulation Models: Comparisons of Harmonic and Inharmonic Analyses
by Haibo Liu, Yan Zhou, Jim Ray and Jiesi Luo
Remote Sens. 2022, 14(15), 3567; https://doi.org/10.3390/rs14153567 - 25 Jul 2022
Cited by 3 | Viewed by 1167
Abstract
Due to the conservation of global angular momentum, polar motion (PM) is dominated by global mass redistributions and relative motions in the atmosphere, oceans and land water at seasonal time scales. Thus, accurately measured PM data can be used to validate the general [...] Read more.
Due to the conservation of global angular momentum, polar motion (PM) is dominated by global mass redistributions and relative motions in the atmosphere, oceans and land water at seasonal time scales. Thus, accurately measured PM data can be used to validate the general circulation models (GCMs) for the atmosphere, oceans and land water. This study aims to analyze geophysical excitations and observed excitations obtained from PM observations from both the harmonic and wavelet analysis perspectives, in order to refine our understanding of the geophysical excitation of PM. The geophysical excitations are derived from two sets of GCMs and a monthly gravity model combining satellite gravity data and some GCM outputs using the PM theory for an Earth model with frequency-dependent responses, while the observed excitation is obtained from the PM data using the frequency-domain Liouville’s equation. Our results show that wavelet analysis can reveal the time-varying nature of all excitations and identify when changes happen and how strong they are, while harmonic analysis can only show the average amplitudes and phases. In particular, the monthly gravity model can correct the mismodeled GCM outputs, while the Earth’s frequency-dependent responses provide us with a better understanding of atmosphere–ocean–land water–solid Earth interactions. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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20 pages, 6743 KiB  
Article
A Hybrid Machine Learning Model Coupling Double Exponential Smoothing and ELM to Predict Multi-Factor Landslide Displacement
by Xing Zhu, Fuling Zhang, Maolin Deng, Junfeng Liu, Zhaoqing He, Wengang Zhang and Xin Gu
Remote Sens. 2022, 14(14), 3384; https://doi.org/10.3390/rs14143384 - 14 Jul 2022
Cited by 5 | Viewed by 1738
Abstract
The deformation of landslides is a non-linear dynamic and complex process due to the impacts of both inherent and external factors. Understanding the basis of landslide deformation is essential to prevent damage to properties and losses of life. To forecast the landslides displacement, [...] Read more.
The deformation of landslides is a non-linear dynamic and complex process due to the impacts of both inherent and external factors. Understanding the basis of landslide deformation is essential to prevent damage to properties and losses of life. To forecast the landslides displacement, a hybrid machine learning model is proposed, in which the Variational Modal Decomposition (VMD) is implemented to decompose the measured total surface displacement into the trend and periodic components. The Double Exponential Smoothing algorithm (DES) and Extreme Learning Machine (ELM) were adopted to predict the trend and the periodic displacement, respectively. Particle Swarm Optimization (PSO) algorithm was selected to obtain the optimal ELM model. The proposed method and implementation procedures were illustrated by a step-like landslide in the Three Gorges Reservoir area. For comparison, Least Square Support Vector Machine (LSSVM) and Convolutional Neutral Network–Gated Recurrent Unit (CNN–GRU) were also conducted with the same dataset to forecast the periodic component. The application results show that DES-PSO-ELM outperformed the other two methods in landslide displacement prediction, with RMSE, MAE, MAPE, and R2 values of 1.295mm, 0.998 mm, 0.008%, and 0.999, respectively. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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33 pages, 18894 KiB  
Article
Geodetic SAR for Height System Unification and Sea Level Research—Results in the Baltic Sea Test Network
by Thomas Gruber, Jonas Ågren, Detlef Angermann, Artu Ellmann, Andreas Engfeldt, Christoph Gisinger, Leszek Jaworski, Tomasz Kur, Simo Marila, Jolanta Nastula, Faramarz Nilfouroushan, Maaria Nordman, Markku Poutanen, Timo Saari, Marius Schlaak, Anna Świątek, Sander Varbla and Ryszard Zdunek
Remote Sens. 2022, 14(14), 3250; https://doi.org/10.3390/rs14143250 - 06 Jul 2022
Cited by 7 | Viewed by 2313
Abstract
Coastal sea level is observed at tide gauge stations, which usually also serve as height reference stations for national networks. One of the main issues with using tide gauge data for sea level research is that only a few stations are connected to [...] Read more.
Coastal sea level is observed at tide gauge stations, which usually also serve as height reference stations for national networks. One of the main issues with using tide gauge data for sea level research is that only a few stations are connected to permanent GNSS stations needed to correct for vertical land motion. As a new observation technique, absolute positioning by SAR using off the shelf active radar transponders can be installed instead. SAR data for the year 2020 are collected at 12 stations in the Baltic Sea area, which are co-located to tide gauges or permanent GNSS stations. From the SAR data, 3D coordinates are estimated and jointly analyzed with GNSS data, tide gauge records and regional geoid height estimates. The obtained results are promising but also exhibit some problems related to the electronic transponders and their performance. At co-located GNSS stations, the estimated ellipsoidal heights agree in a range between about 2 and 50 cm for both observation systems. From the results, it can be identified that, most likely, variable systematic electronic instrument delays are the main reason, and that each transponder instrument needs to be calibrated individually. Nevertheless, the project provides a valuable data set, which offers the possibility of enhancing methods and procedures in order to develop a geodetic SAR positioning technique towards operability. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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19 pages, 7306 KiB  
Article
The Drought Events over the Amazon River Basin from 2003 to 2020 Detected by GRACE/GRACE-FO and Swarm Satellites
by Lilu Cui, Maoqiao Yin, Zhengkai Huang, Chaolong Yao, Xiaolong Wang and Xu Lin
Remote Sens. 2022, 14(12), 2887; https://doi.org/10.3390/rs14122887 - 16 Jun 2022
Cited by 10 | Viewed by 1908
Abstract
The climate anomaly in the Amazon River basin (ARB) has a very important influence on global climate change and has always been the focus of scientists from all over the world. To fill the 11-month data gap between Gravity Recovery and Climate Experiment [...] Read more.
The climate anomaly in the Amazon River basin (ARB) has a very important influence on global climate change and has always been the focus of scientists from all over the world. To fill the 11-month data gap between Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, we fused the TWSC results from six GRACE solutions by using the generalized three-cornered hat and the least square method to improve the reliability of TWSC results, and then combined Swarm data to construct an uninterrupted long time series of a TWSC-based drought index (GRACE/Swarm-DSI). The drought index was used to detect and characterize the drought events in the ARB between 2003 and 2020. The results show that GRACE/Swarm-DSI has a strong correlation with Self-Calibrating Palmer Drought Severity Index (SCPDSI) (0.6345), Standardized Precipitation Evapotranspiration Index-3 (SPEI-3) (0.5411), SPEI-6 (0.6377) and SPEI-12 (0.6820), and the Nash–Sutcliffe efficiency between GRACE/Swarm-DSI and the above four drought indices are 0.3348, 0.2786, 0.4044 and 0.4627, respectively. Eleven drought events were identified in the ARB during the study period, and the 2005, 2010 and 2016 droughts are the most severe and the longest. The correlation between GRACE/Swarm-DSI and precipitation (PPT) (the correlation coefficient is 0.55 with a 2-month delay) is higher than that of evapotranspiration (ET) (the correlation coefficient is −0.18 with a 12-month delay). It explains that less PPT is the main cause of drought events in the ARB. The influence of PPT is greater in the plains than the one in the mountains and the response time of GRACE/Swarm-DSI to PPT is 1~2 months in most regions. Our results provide a certain reference for the hydrological application of the Swarm model in filling the gap between GRACE and GRACE-FO missions. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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11 pages, 2974 KiB  
Communication
Noise Analysis and Combination of Hydrology Loading-Induced Displacements
by Chang Xu, Xin Yao and Xiaoxing He
Remote Sens. 2022, 14(12), 2840; https://doi.org/10.3390/rs14122840 - 14 Jun 2022
Cited by 1 | Viewed by 1214
Abstract
Large uncertainties exist in the available hydrology loading prediction models, and currently no consensus is reached on which loading model is superior or appears to represent nature in a more satisfactory way. This study discusses the noise characterization and combination of the vertical [...] Read more.
Large uncertainties exist in the available hydrology loading prediction models, and currently no consensus is reached on which loading model is superior or appears to represent nature in a more satisfactory way. This study discusses the noise characterization and combination of the vertical loadings predicted by different hydrology reanalysis (e.g., MERRA, GLDAS/Noah, GEOS-FPIT, and ERA interim). We focused on the hydrology loading predictions in the time span from 2011 to 2014 for the 70 Global Positioning System (GPS) sites, which are located close to the great rivers, lakes, and reservoirs. The maximum likelihood estimate with Akaike information criteria (AIC) showed that the auto-regressive (AR) model with an order from 2 to 5 is a good description of the temporal correlation that exists in the hydrology loading predictions. Moreover, significant discrepancy exists in the root mean square (RMS) of different hydrology loading predictions, and none of them have the lowest noise level for the all-time domain. Principal component analysis (PCA) was therefore used to create a combined loading-induced time series. Statistical indices (e.g., mean overlapping Hadamard variance, Nash-Sutcliffe efficiency, and variance reduction) showed that our proposed algorithm had an overall good performance and seemed to be potentially feasible for performing corrections on geodetic GPS heights. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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18 pages, 18143 KiB  
Article
A Comprehensive Analysis of Environmental Loading Effects on Vertical GPS Time Series in Yunnan, Southwest China
by Shunqiang Hu, Kejie Chen, Hai Zhu, Changhu Xue, Tan Wang, Zhenyu Yang and Qian Zhao
Remote Sens. 2022, 14(12), 2741; https://doi.org/10.3390/rs14122741 - 07 Jun 2022
Cited by 5 | Viewed by 2494
Abstract
Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison [...] Read more.
Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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22 pages, 8675 KiB  
Article
Displacement Prediction of the Muyubao Landslide Based on a GPS Time-Series Analysis and Temporal Convolutional Network Model
by Da Huang, Jun He, Yixiang Song, Zizheng Guo, Xiaocheng Huang and Yingquan Guo
Remote Sens. 2022, 14(11), 2656; https://doi.org/10.3390/rs14112656 - 01 Jun 2022
Cited by 9 | Viewed by 2038
Abstract
Landslide displacement prediction is an essential base of landslide hazard prevention, which often needs to establish an accurate prediction model. To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed. The TCN [...] Read more.
Landslide displacement prediction is an essential base of landslide hazard prevention, which often needs to establish an accurate prediction model. To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed. The TCN model, consisting of a causal dilation convolution layer residual block, can flexibly increase the receptive fields and capture the global information in a deeper layer. SSA can solve the hyperparameter problem well for TCN model. The Muyubao landslide displacement collected from a professional GPS monitoring system implemented in 2006 is used to analyze the displacement features of the slope and evaluate the performance of the SSA-TCN model. The cumulative displacement time series is decomposed into trend displacement (linear part) and periodic displacement (nonlinear part) by the variational modal decomposition (VMD) method. Then, a polynomial function is used to predict the trend displacement, and the SSA-TCN model is used to predict the periodic displacement of the landslide based on considering the response relationship between periodic displacement, rainfall, and reservoir water. This research also compares the proposed approach results with the other popular machine learning and deep learning models. The results demonstrate that the proposed hybrid model is superior to and more effective and accurate than the others at predicting the landslide displacement. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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13 pages, 7371 KiB  
Communication
Using Range Split-Spectrum Interferometry to Reduce Phase Unwrapping Errors for InSAR-Derived DEM in Large Gradient Region
by Wenfei Mao, Guoxiang Liu, Xiaowen Wang, Yakun Xie, Xiaoxing He, Bo Zhang, Wei Xiang, Shuaiying Wu, Rui Zhang, Yin Fu and Saied Pirasteh
Remote Sens. 2022, 14(11), 2607; https://doi.org/10.3390/rs14112607 - 29 May 2022
Cited by 3 | Viewed by 1677
Abstract
The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution [...] Read more.
The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM’s accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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21 pages, 10214 KiB  
Article
An Approach for Predicting Global Ionospheric TEC Using Machine Learning
by Jun Tang, Yinjian Li, Dengpan Yang and Mingfei Ding
Remote Sens. 2022, 14(7), 1585; https://doi.org/10.3390/rs14071585 - 25 Mar 2022
Cited by 19 | Viewed by 2591
Abstract
Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict [...] Read more.
Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE’S 1-Day (C1PG) and CODE’S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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16 pages, 6320 KiB  
Article
A New Spatial Filtering Algorithm for Noisy and Missing GNSS Position Time Series Using Weighted Expectation Maximization Principal Component Analysis: A Case Study for Regional GNSS Network in Xinjiang Province
by Wudong Li, Zhao Li, Weiping Jiang, Qusen Chen, Guangbin Zhu and Jian Wang
Remote Sens. 2022, 14(5), 1295; https://doi.org/10.3390/rs14051295 - 07 Mar 2022
Cited by 9 | Viewed by 1909
Abstract
Common Mode Error (CME) presents a kind of spatially correlated error that is widespread in regional Global Navigation Satellite System (GNSS) networks and should be eliminated during postprocessing of a GNSS position time series. Several spatiotemporal filtering methods have been developed to mitigate [...] Read more.
Common Mode Error (CME) presents a kind of spatially correlated error that is widespread in regional Global Navigation Satellite System (GNSS) networks and should be eliminated during postprocessing of a GNSS position time series. Several spatiotemporal filtering methods have been developed to mitigate the effects of CME. However, such methodologies become inappropriate when missing and noisy data exists. In this research, we introduce a novel spatial filtering algorithm called Weighted Expectation Maximization Principal Component Analysis (WEMPCA) for detecting and removing CME from noisy GNSS position time series with missing values, among which formal errors of daily GNSS solutions are utilized to weight the input data. Compared with traditional PCA and the special case of EMPCA, simulation experiments demonstrate that the new WEMPCA algorithm always has outstanding performance over others. The WEMPCA algorithm was then successfully used to extract the CME from real noisy and missing GNSS position time series in Xinjiang province. Our results show that only the first principal component exhibits significant spatial response, with average values of 70.11%, 66.53%, and 52.45% for North, East, and Up (NEU) components, respectively, indicating that it represents the CME of this region. After removing CME, the canonical correlation coefficients and root mean square error of GNSS residual time series, as well as the amplitudes of power-law noises (PLN), are obviously decreased in all three directions. However, the white noise (WN) amplitudes are found to diminish exclusively in the North and East component, not in the Up components. Moreover, the average velocity differences before and after filtering CME are 0.19 mm/year, 0.03 mm/year, and −0.56 mm/year for the NEU components, respectively, indicating that CME has an influence on the GNSS station velocity estimation. The velocity uncertainty is also reduced by 43.51%, 38.64%, and 40.39% on average for the NEU components, respectively, implying that the velocity estimates are more reliable and accurate after removing CME. Therefore, we conclude that the new WEMPCA approach provides an efficient solution to detect and mitigate CME from the noisy and missing GNSS position time series. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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17 pages, 4347 KiB  
Article
Assessment of Contemporary Antarctic GIA Models Using High-Precision GPS Time Series
by Wenhao Li, Fei Li, C.K. Shum, Chanfang Shu, Feng Ming, Shengkai Zhang, Qingchuan Zhang and Wei Chen
Remote Sens. 2022, 14(5), 1070; https://doi.org/10.3390/rs14051070 - 22 Feb 2022
Cited by 1 | Viewed by 1934
Abstract
Past redistributions of the Earth’s mass resulting from the Earth’s viscoelastic response to the cycle of deglaciation and glaciation reflect the process known as glacial isostatic adjustment (GIA). GPS data are effective at constraining GIA velocities, provided that these data are accurate, have [...] Read more.
Past redistributions of the Earth’s mass resulting from the Earth’s viscoelastic response to the cycle of deglaciation and glaciation reflect the process known as glacial isostatic adjustment (GIA). GPS data are effective at constraining GIA velocities, provided that these data are accurate, have adequate spatial coverage, and account for competing geophysical processes, including the elastic loading of ice/snow ablation/accumulation. GPS solutions are significantly affected by common mode errors (CMEs) and the choice of optimal noise model, and they are contaminated by other geophysical signals due primarily to the Earth’s elastic response. Here, independent component analysis is used to remove the CMEs, and the Akaike information criterion is used to determine the optimal noise model for 79 GPS stations in Antarctica, primarily distributed across West Antarctica and the Antarctic Peninsula. Next, a high-resolution surface mass variation model is used to correct for elastic deformation. Finally, we use the improved GPS solution to assess the accuracy of seven contemporary GIA forward models in Antarctica. The results show that the maximal GPS crustal displacement velocity deviations reach 4.0 mm yr−1, and the mean variation is 0.4 mm yr−1 after removing CMEs and implementing the noise analysis. All GIA model-predicted velocities are found to systematically underestimate the GPS-observed velocities in the Amundsen Sea Embayment. Additionally, the GPS vertical velocities on the North Antarctic Peninsula are larger than those on the South Antarctic Peninsula, and most of the forward models underestimate the GIA impact on the Antarctic Peninsula. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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22 pages, 3171 KiB  
Article
A New Way for Cartesian Coordinate Transformation and Its Precision Evaluation
by Ronghua Yang, Chang Deng, Kegen Yu, Zhao Li and Leixilan Pan
Remote Sens. 2022, 14(4), 864; https://doi.org/10.3390/rs14040864 - 11 Feb 2022
Cited by 6 | Viewed by 1688
Abstract
High-precision coordinate transformation is vital for high-quality data fusion involving different coordinate systems. The transformation precision is mainly evaluated by the transformation parameters’ estimation precision, the root mean square error (RMSE) of the conversion of common points, or the RMSE of the conversion [...] Read more.
High-precision coordinate transformation is vital for high-quality data fusion involving different coordinate systems. The transformation precision is mainly evaluated by the transformation parameters’ estimation precision, the root mean square error (RMSE) of the conversion of common points, or the RMSE of the conversion of check points. However, there are a number of issues associated with the rotation parameters’ precision estimated by the existing transformation methods. First, the estimated precision is related to the rotation matrix, so it is not suitable for scenarios where different rotation matrices are used. Second, the RMSE of the conversion of check points may not be consistent with the RMSE of the conversion of common points, so that the RMSE of the conversion of common points should not be used as a transformation precision index. In addition, some engineering applications do not have check points, and many applications need to know which range of points can meet our requirements. To deal with these limitations, this paper proposes a new way to calculate the translation parameters and evaluate the transformation precision. A lot of experimental data was used to verify the effectiveness and applicability of the proposed transformation model. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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20 pages, 7286 KiB  
Article
Consistency Analysis of the GNSS Antenna Phase Center Correction Models
by Renyu Zhou, Zhigang Hu, Qile Zhao, Hongliang Cai, Xuanzuo Liu, Chengyi Liu, Guangxing Wang, Haoyu Kan and Liang Chen
Remote Sens. 2022, 14(3), 540; https://doi.org/10.3390/rs14030540 - 23 Jan 2022
Cited by 7 | Viewed by 2916
Abstract
For the same antenna type, numerical differences among the phase center correction (PCC) models released by different institutes can reach several millimeters, which is far beyond the nominal calibration accuracy. This contribution introduces a new method to evaluate the consistency of [...] Read more.
For the same antenna type, numerical differences among the phase center correction (PCC) models released by different institutes can reach several millimeters, which is far beyond the nominal calibration accuracy. This contribution introduces a new method to evaluate the consistency of these PCC models. We first investigated the coupling of phase center offset (PCO) and phase center variation (PCV) through simulation experiments, and the results show that the calibrated PCO values under different strategies may result in large differences, and so do PCV values due to strong coupling with PCO. This is further confirmed by field calibration experiments. Moreover, a new datum parameter is introduced to equivalently transform the PCC models to assess the consistency of PCC models of the same antenna type calibrated under different strategies or by different facilities. It is also essential to perform consistency analysis of PCC models in the coordinate domain. We further investigated these PCC models through a simulated positioning experiment. The results show that millimeter-level consistency of PCC models will lead to the same level of positioning precision in the coordinate domain. Moreover, as a comparison, both baseline positioning and PPP were performed with an antenna-type JAV_RINGANT_G3T NONE based on real observations. Multiday results showed that the average RMS of the positioning differences between PCC models from robot and anechoic chamber calibration is less than 1 mm for the baseline solution and 4 mm for the PPP solution, although the PCC model differences can reach 6 mm in L1 and 4 mm in L2, respectively. Finally, we also investigated the distribution of position biases without PCC or with inaccurate PCC. Considering the actual GPS constellation, we found that position biases have a strong correlation with latitude, if PCV values fluctuate greatly with the users’ elevation angle. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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16 pages, 43985 KiB  
Article
Analysis of Ionospheric Disturbance Response to the Heavy Rain Event
by Jian Kong, Lulu Shan, Xiao Yan and Youkun Wang
Remote Sens. 2022, 14(3), 510; https://doi.org/10.3390/rs14030510 - 21 Jan 2022
Cited by 4 | Viewed by 2082
Abstract
Meteorological activities in the troposphere would affect electron concentrations and distributions in the ionosphere, thereby exciting ionospheric disturbance. To explore the ionospheric anomalies during severe convective weather, the ionospheric phenomenon during the heavy rainfall in Sichuan Province on 9 July 2013 was analyzed [...] Read more.
Meteorological activities in the troposphere would affect electron concentrations and distributions in the ionosphere, thereby exciting ionospheric disturbance. To explore the ionospheric anomalies during severe convective weather, the ionospheric phenomenon during the heavy rainfall in Sichuan Province on 9 July 2013 was analyzed based on GNSS data. The Total Electron Content (TEC) are evaluated by carrier phase smoothed pseudoranges. Then, the dTEC (detrend TEC) sequences are obtained by using the cubic smoothing spline. They show obvious N-shaped ionospheric disturbances and have propagation characteristics, with the maximum of 0.4 TECU. Frequency domain analysis using continuous wavelet transform (CWT) also reached similar conclusions—that there are obvious ionospheric disturbances with different frequencies and intensity. Based on the isotropic assumption and feature points method, the horizontal propagation velocity of the disturbances in the ionosphere is estimated to be approximately 150 m/s. Then, Sichuan Province is divided into 1° × 1° grids, and the disturbance trigger source is determined via the grid searching method to be the central of Sichuan Province. Finally, the mechanisms causing ionospheric disturbance are discussed. During the heavy rainfall, the strong convection may excite gravity waves (GWs), which are driven by terrain and background wind fields to propagate upwards to the ionosphere and release energy, causing ionospheric disturbances. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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20 pages, 9456 KiB  
Article
GNSS Imaging of Strain Rate Changes and Vertical Crustal Motions over the Tibetan Plateau
by Yunfei Xiang, Hao Wang, Yuanyuan Chen and Yin Xing
Remote Sens. 2021, 13(23), 4937; https://doi.org/10.3390/rs13234937 - 04 Dec 2021
Cited by 2 | Viewed by 2169
Abstract
In this paper, we perform a comprehensive analysis of contemporary three-dimensional crustal deformations over the Tibetan Plateau. Considering that the coverage of continuous GNSS sites in the Tibetan Plateau is sparse, a newly designed method that mainly contains Spatial Structure Function (SSF) construction [...] Read more.
In this paper, we perform a comprehensive analysis of contemporary three-dimensional crustal deformations over the Tibetan Plateau. Considering that the coverage of continuous GNSS sites in the Tibetan Plateau is sparse, a newly designed method that mainly contains Spatial Structure Function (SSF) construction and Median Spatial Filtering (MSF) is adopted to conduct GNSS imaging of point-wise velocities, which can well reveal the spatial pattern of vertical crustal motions. The result illustrates that the Himalayan belt bordering Nepal appears significant uplift at the rates of ~3.5 mm/yr, while the low-altitude regions of Nepal and Bhutan near the Tibetan Plateau are undergoing subsidence. The result suggests that the subduction of the Indian plate is the driving force of the uplift and subsidence in the Himalayan belt and its adjacent regions. Similarly, the thrusting of the Tarim Basin is the main factor of the slight uplift and subsidence in the Tianshan Mountains and Tarim Basin, respectively. In addition, we estimate the strain rate changes over the Tibetan Plateau using high-resolution GNSS horizontal velocities. The result indicates that the Himalayan belt and southeastern Tibetan Plateau have accumulated a large amount of strain rate due to the Indian-Eurasian plate collision and blockage of the South China block, respectively. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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14 pages, 6869 KiB  
Technical Note
Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis
by Bin Liu, Wenkun Yu, Wujiao Dai, Xuemin Xing and Cuilin Kuang
Remote Sens. 2022, 14(2), 282; https://doi.org/10.3390/rs14020282 - 08 Jan 2022
Cited by 9 | Viewed by 1799
Abstract
GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) [...] Read more.
GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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