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New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling

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

Deadline for manuscript submissions: 15 January 2025 | Viewed by 13387

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Dr. Angela Aragón-Ángel

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Research Group of Astronomy and GEomatics (gAGE), Mathematics Department, Universitat Politècnica de Catalunya (UPC), C/ Jordi Girona 1-3, Campus Nord UPC, Building C3, 08034 Barcelona, Spain
Interests: ionosphere; GNSS; navigation; Galileo ICA; GPS ICA; remote sensing; space weather

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to highlight the latest and novel contributions of global navigation satellite systems (GNSS)-derived data to the monitoring, modeling and study of the upper ionized region of the Earth’s atmosphere: the ionosphere. The ionosphere has become a key player in our everyday communications and navigation systems. Radio and GNSS signals travel through this layer of the atmosphere to reach their destinations being disrupted by changes in the ionosphere’s density and its composition. The increasing number of openly available ground- and space-based GNSS observations from current and planned observing systems, as well as distributed arrays of small instruments open new opportunities for deepening the research of the ionosphere and consolidating knowledge on the physics principles governing it.

This Special Issue aims to publish studies covering diﬀerent uses of GNSS data acquired by diﬀerent sensors and platforms, but also analysis and modeling techniques for a better understanding of the ionospheric processes. Hence, multisource data integration, multiscale approaches or studies focused on ionospheric weather services monitoring, among other issues, are welcome. Articles may address, but are not limited to, the following topics:

Algorithm and methodology development for ionospheric determination;
GNSS-based ionospheric observing, monitoring and modeling at diﬀerent scales (local, regional and global);
GNSS techniques for ionospheric space weather services;
GNSS techniques in high accuracy positioning;
Data analysis techniques and modeling that extend the capability of current observing systems are also welcome.

Dr. Angela Aragón-Ángel
Guest Editor

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 ground- and space-based observations
electron density morphology and determination
ionospheric indices
ionospheric models
ionospheric storms
low-, mid- and high-latitude ionosphere
waves and irregularities
total electron content (TEC)
ionospheric global and regional maps
extreme events
GNSS navigation

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Published Papers (9 papers)

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23 pages, 9085 KiB

Open AccessArticle

Real-Time Detection and Correction of Abnormal Errors in GNSS Observations on Smartphones

by Hongbo Mu, Xianwen Yu, Angela Aragon-Angel, Jiafu Wang and Yanze Wu

Remote Sens. 2024, 16(17), 3117; https://doi.org/10.3390/rs16173117 - 23 Aug 2024

Viewed by 554

Abstract

Smartphones, due to the integration of low-cost GNSS chips and linearly polarized antennas, frequently experience abnormal errors in their observations, particularly during positioning on water surfaces. In response to this issue, this paper proposes a method for detecting and correcting abnormal errors in GNSS observations on smartphones. Firstly, the state and observation equations of the Kalman filter are formulated based on the continuous and smooth characteristics of pseudorange and carrier observations. Secondly, real-time detection of abnormal error occurrence in observations is performed by assessing whether the difference between the predicted and observed values computed by the Kalman filter exceeds a specified threshold. Finally, depending on abnormal errors within the epoch, different strategies are applied for real-time reparation of observations containing anomalies. Two smartphones have been used for static tests on land and kinematic tests on water. Results show that under various environmental conditions, the proposed method effectively enhances the quality of observations on smartphones. Specifically, the method achieved a maximum improvement of 86.03% in pseudorange quality and 84.31% in carrier phase quality. The method proposed in this paper outperformed the State-Based method by approximately 10% on land and by 10–35% on water. It also shows high stability and reliability, particularly in complex environments such as navigation on water. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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25 pages, 4138 KiB

Open AccessArticle

An EOF-Based Global Plasmaspheric Electron Content Model and Its Potential Role in Vertical-Slant TEC Conversion

by Fengyang Long, Chengfa Gao, Yanfeng Dong and Zhenhao Xu

Remote Sens. 2024, 16(11), 1857; https://doi.org/10.3390/rs16111857 - 23 May 2024

Viewed by 639

Abstract

Topside total electron content (TEC) data measured by COSMIC/FORMAT-3 during 2008 and 2016 were used to analyze and model the global plasmaspheric electron content (PEC) above 800 km with the help of the empirical orthogonal function (EOF) analysis method, and the potential role of the proposed PEC model in helping Global Navigation Satellite System (GNSS) users derive accurate slant TEC (STEC) from existing high-precision vertical TEC (VTEC) products was validated. A uniform gridded PEC dataset was first obtained using the spherical harmonic regression method, and then, it was decomposed into EOF basis modes. The first four major EOF modes contributed more than 99% of the total variance. They captured the pronounced latitudinal gradient, longitudinal differences, hemispherical differences, diurnal and seasonal variations, and the solar activity dependency of global PEC. A second-layer EOF decomposition was conducted for the spatial pattern and amplitude coefficients of the first-layer EOF modes, and an empirical PEC model was constructed by fitting the second-layer basis functions related to latitude, longitude, local time, season, and solar flux. The PEC model was designed to be driven by whether solar proxy or parameters derived from the Klobuchar model meet the real-time requirements. The validation of the results demonstrated that the proposed PEC model could accurately simulate the major spatiotemporal patterns of global PEC, with a root-mean-square (RMS) error of 1.53 and 2.24 TECU, improvements of 40.70% and 51.74% compared with NeQuick2 model in 2009 and 2014, respectively. Finally, the proposed PEC model was applied to conduct a vertical-slant TEC conversion experiment with high-precision Global Ionospheric Maps (GIMs) and dual-frequency carrier phase observables of more than 400 globally distributed GNSS sites. The results of the differential STEC (dSTEC) analysis demonstrated the effectiveness of the proposed PEC model in aiding precise vertical-slant TEC conversion. It improved by 18.52% in dSTEC RMS on a global scale and performed better in 90.20% of the testing days compared with the commonly used single-layer mapping function. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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17 pages, 22587 KiB

Open AccessArticle

Effects of Strong Geomagnetic Storms on the Ionosphere and Degradation of Precise Point Positioning Accuracy during the 25th Solar Cycle Rising Phase: A Case Study

by Yifan Wang, Yunbin Yuan, Min Li, Ting Zhang, Hao Geng, Guofang Wang and Gang Wen

Remote Sens. 2023, 15(23), 5512; https://doi.org/10.3390/rs15235512 - 27 Nov 2023

Cited by 6 | Viewed by 2442

Abstract

Approaching the peak year of the 25th solar activity cycle, the frequency of strong geomagnetic storms is gradually increasing, which seriously affects the navigation and positioning performance of GNSS. Based on the globally distributed GNSS station data and FORMOSAT-7/COSMIC-2 occultation data, this paper explores for the first time the effects of the G4-class geomagnetic storm that occurred on 23–24 April 2023 on the global ionosphere, especially the ionospheric equatorial anomalies and F-layer perturbations. It reveals the precise point positioning (PPP) accuracy degradation during a geomagnetic storm. The results show that the ionospheric rate of total electron content index (ROTI) and near high latitude GNSS phase scintillations index have varying levels of perturbation during geomagnetic storms, with the maximum ROTI and phase scintillations index exceeding 0.5 TECU/min and 0.8, respectively. The equatorial ionization anomaly (EIA) shows an enhanced state (positive ionospheric storms) during geomagnetic storms, and the cause of this phenomenon is most likely the equatorward neutral wind. The variation of the S4 index of the FORMOSAT-7/COSMIC-2 satellite reveals the uplift of the F-layer during geomagnetic storms. During geomagnetic storms, the PPP accuracy degrades most seriously at high latitudes, the maximum MAE exceeds 2.3 m, and the RMS in the three-dimensional (3D) direction exceeds 2.0 m. These investigations can provide case support for space weather and GNSS studies of the impact of geomagnetic storms during peak solar activity years. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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17 pages, 28965 KiB

Open AccessArticle

Analysis of Ionospheric Disturbances during X-Class Solar Flares (2021–2022) Using GNSS Data and Wavelet Analysis

by Charbeth López-Urias, G. Esteban Vazquez-Becerra, Karan Nayak and Rebeca López-Montes

Remote Sens. 2023, 15(18), 4626; https://doi.org/10.3390/rs15184626 - 20 Sep 2023

Cited by 10 | Viewed by 1622

Abstract

The influence of solar activity on the ionosphere, a critical area of investigation due to its relevance to the Sun–Earth relationship, has been extensively examined through various methodologies. The ability of solar events to induce disturbances in both the ionosphere and the geomagnetic field is widely acknowledged. This specific study focused on sporadic incidents resulting from X-class solar flares that occurred between 2021 and 2022. Utilizing a methodology that involved analyzing data at 5Hz intervals using wavelet algorithms, the data from the GNSS stations of the National Autonomous University of Mexico (UNAM) were investigated. The primary emphasis was on deducing the Total Electron Content (TEC) within the ionosphere. Subsequently, this parameter for each satellite during instances of solar flares was analyzed. The approach uncovered disruptions in the ionosphere triggered by solar flares, even in cases where events transpired at the periphery of the solar disk and were of magnitudes smaller than X2. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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21 pages, 14067 KiB

Open AccessArticle

Comparative Analysis of the H2PT Ionosphere Model

by Paulina Gajdowska, Anna Świątek, Łukasz Tomasik and Mariusz Pożoga

Remote Sens. 2023, 15(18), 4478; https://doi.org/10.3390/rs15184478 - 12 Sep 2023

Viewed by 797

Abstract

The ionosphere stands in the path of signals emitted by Global Navigation Satellite System (GNSS) satellites to receivers located on the Earth’s surface. Many factors affect the accuracy of satellite positioning, but error due to ionospheric refraction is the largest among them. For this reason, it is important to minimize the impact of ionospheric refraction, and ionospheric models are one of the methods used. As the intensity of the processes taking place in the ionosphere is variable because of solar activity, the influence on satellite observations is also not constant; it varies by location and time of day and year. Therefore, models focusing on the region of interest to users are especially useful in precise GNSS applications. In this research, the H2PT model covering the region of Europe was examined at a temporal resolution of 15 min and two spatial resolutions (latitude × longitude) of 1° × 1° and 5° × 5°. This study aimed to compare the H2PT model with the solution obtained from the International GNSS Service (IGS) in the context of vertical total electron content (VTEC). The H2PT values in high-latitude regions turned out to be overestimated compared to IGS VTEC maps, while, in low-latitude regions, the situation was the opposite. Although the differences between the analyzed maps were usually a few TECUs, it was observed that, during the course of a day, they could increase to several dozen TECUs. Furthermore, the data from selected days characterized by high or low activity of the ionosphere were subjected to a detailed analysis (in relation to quiet days, as well as to the median). The data available with a 15-min interval allowed the identification of short-term disturbances appearing in the ionosphere. The analyzed model, which is of a regional nature and has a relatively high resolution, allows improvement to be made to the quality of the determined ionospheric correction in GNSS positioning. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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17 pages, 7811 KiB

Open AccessArticle

Assimilating GNSS TEC with an LETKF over Yunnan, China

by Jun Tang, Shimeng Zhang, Dengpan Yang and Xuequn Wu

Remote Sens. 2023, 15(14), 3547; https://doi.org/10.3390/rs15143547 - 14 Jul 2023

Cited by 1 | Viewed by 1105

Abstract

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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21 pages, 2715 KiB

Open AccessArticle

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

by Artem Kharakhashyan and Olga Maltseva

Remote Sens. 2023, 15(12), 3069; https://doi.org/10.3390/rs15123069 - 12 Jun 2023

Cited by 1 | Viewed by 1496

Abstract

Machine learning can play a significant role in bringing new insights in GNSS remote sensing for ionosphere monitoring and modeling to service. In this paper, a set of multilayer architectures of neural networks is proposed and considered, including both neural networks based on LSTM and GRU, and temporal convolutional networks. The set of methods included 10 architectures: TCN, modified LSTM-/GRU-based deep networks, including bidirectional ones, and BiTCN. The comparison of TEC forecasting accuracy is performed between individual architectures, as well as their bidirectional modifications, by means of MAE, MAPE, and RMSE estimates. The F10.7, 10 Kp, Np, Vsw, and Dst indices are used as predictors. The results are presented for the reference station Juliusruh, three stations along the meridian 30°E (Murmansk, Moscow, and Nicosia), and three years of different levels of solar activity (2015, 2020, and 2022). The MAE and RMSE values depend on the station latitude, following the solar activity. The conventional LSTM and GRU networks with the proposed modifications and the TCN provide results at the same level of accuracy. The use of bidirectional neural networks significantly improves forecast accuracy for all the architectures and all stations. The best results are provided by the BiTCN architecture, with MAE values less than 0.3 TECU, RMSE less than 0.6 TECU, and MAPE less than 5%. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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16 pages, 2143 KiB

Open AccessTechnical Note

On Some Challenges for National and Global Space Weather Services

by Maria A. Sergeeva, Juan Americo Gonzalez-Esparza, Victor Jose Gatica-Acevedo, Luis Xavier Gonzalez, Pedro Corona-Romero, Ernesto Aguilar-Rodriguez, Angela Melgarejo-Morales, Isaac David Orrala-Legorreta, Julio Cesar Mejia-Ambriz and Jose Juan Gonzalez-Aviles

Remote Sens. 2023, 15(19), 4839; https://doi.org/10.3390/rs15194839 - 6 Oct 2023

Viewed by 955

Abstract

Space Weather (SW) hazards are discussed in terms of the operation of national SW services and global SW centers for the International Civil Aviation Organization (ICAO). The definition of threshold values of monitored parameters which are used to identify moderate and severe SW events is one of the critical problems. Due to the lack of both physical data on severe events and user feedback, we tried to approach the problem statistically. In particular, we pursued the answer to the question about what intensity of ionospheric storms and flare effects should be reported by national and global SW entities to their users. We also discussed the possible role of an active region on the Sun, and the cosmic rays’ issues that may be helpful regarding SW operational work. The presented considerations are based on examples of the ionosphere state assessment for the low-latitude American sector with a focus on the Mexican region. This work attempts to argue the possible approaches to resolve the tasks that the SW national services and global centers face. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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16 pages, 2677 KiB

Open AccessTechnical Note

Topside Ionospheric Tomography Exclusively Based on LEO POD GPS Carrier Phases: Application to Autonomous LEO DCB Estimation

by Manuel Hernández-Pajares, Germán Olivares-Pulido, M. Mainul Hoque, Fabricio S. Prol, Liangliang Yuan, Riccardo Notarpietro and Victoria Graffigna

Remote Sens. 2023, 15(2), 390; https://doi.org/10.3390/rs15020390 - 8 Jan 2023

Cited by 2 | Viewed by 2366

Abstract

This paper presents a novel technique to estimate DCBs from GPS transmitters and receivers on-board Low Earth Orbit (LEO) satellites. The technique consists of obtaining the DCBs as residuals from the difference between the ionospheric combination of the code and the associated ionospheric delay. The ionospheric delay is computed with TOMION, a background-model-free ionospheric tomographic technique based on dual-frequency GPS carrier phase data only, and solved with a Kalman filter. Thus, DCBs are also estimated epoch-wise from the LEO Precise Orbit Determination (POD) GPS receiver as a secondary product. The results for GPS satellite DCBs, obtained exclusively from the three MetOp LEO POD GPS receivers over four consecutive weeks, are in full agreement (i.e., at the level of a few tenths of ns) with those reported independently with other techniques from hundreds of ground-based receivers exclusively, by JPL and CODE analysis centers. Full article

(This article belongs to the Special Issue New Insights in GNSS Remote Sensing for Ionosphere Monitoring and Modeling)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Transformer-based Ionospheric Prediction and Interpretability Analysis for Enhanced GNSS Positioning
Author: Wang
Highlights: 1. Application of Transformer deep learning model in ionospheric prediction for improving GNSS positioning accuracy. 2. Analyzes the prediction mechanism through the integrated gradient method. 3. Emphasizes the impact of the parameters of the GIM (global ionospheric models) on VTEC prediction accuracy:

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