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Advances in GNSS Remote Sensing for Ionosphere Observation
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Advances in GNSS Remote Sensing for Ionosphere Observation

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 7943

Special Issue Editors

Dr. Wang Li

E-Mail Website1 Website2
Guest Editor
Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Interests: space weather; ionospheric modeling; ionospheric disturbances
Special Issues, Collections and Topics in MDPI journals
Dr. Dongsheng Zhao

E-Mail Website
Guest Editor
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: GNSS remote sensing; ionospheric scintillation
Special Issues, Collections and Topics in MDPI journals
Dr. Dunyong Zheng

E-Mail Website
Guest Editor
School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Interests: GNSS ionospheric tomography; ionospheric disturbances
Dr. Long Tang

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: ionospheric irregularities and disturbances; GNSS precise positioning
Dr. Andong Hu

E-Mail Website
Guest Editor
Cooperative Institute for Research in Environmental Sciences (CIRES), CU Boulder, Boulder, CO 80309, USA
Interests: machine learning; space weather; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ionosphere plays a critical role in Earth's atmospheric dynamics, significantly influencing satellite communication, GPS systems, and radio wave propagation. Understanding its characteristics in complex space environments is crucial for both theoretical advancements and practical applications. This Special Issue focuses on recent developments in remote sensing technologies and methodologies used to observe and study the ionosphere, highlighting advancements in both ground-based and space-based tools, such as ionosondes, GPS-based total electron content (TEC) measurements, and satellite missions that improve the accuracy of ionospheric monitoring.

This collection of studies marks a major step forward in understanding the ionosphere's complex processes, contributing to more reliable space weather forecasting models and enhancing the performance of communication and navigation systems. Suggested themes for submissions include, but are not limited to, the integration of multi-source data for comprehensive ionospheric modeling, new algorithms for detecting ionospheric irregularities, and the application of advanced signal processing techniques. The issue also examines the effects of space weather on ionospheric disturbances, offering insights into how solar activity and geomagnetic storms impact the ionosphere.

This Special Issue invites original research articles, reviews, methodologies, and case studies that showcase innovative approaches, novel findings, and practical applications in ionospheric research.

Dr. Wang Li
Dr. Dongsheng Zhao
Dr. Dunyong Zheng
Dr. Long Tang
Dr. Andong Hu
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

  • deep learning
  • ionospheric modeling
  • space weather
  • ionospheric disturbances
  • ionospheric scintillation
  • lithosphere-ionosphere coupling

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Published Papers (4 papers)

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Research

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20 pages, 4039 KiB  
Article
Ionospheric TEC and ROT Analysis with Signal Combinations of QZSS Satellites in the Korean Peninsula
by Byung-Kyu Choi, Dong-Hyo Sohn, Junseok Hong, Jong-Kyun Chung, Kwan-Dong Park, Hyung Keun Lee, Jeongrae Kim and Heon Ho Choi
Remote Sens. 2025, 17(11), 1945; https://doi.org/10.3390/rs17111945 - 4 Jun 2025
Viewed by 258
Abstract
This study investigates the performance of three different signal combinations (L1-L2, L1-L5, and L2-L5) for estimating ionospheric total electron content (TEC) and the rate of TEC (ROT) using Quasi-Zenith Satellite System (QZSS) observations over the Korean Peninsula. GNSS data collected from nine stations [...] Read more.
This study investigates the performance of three different signal combinations (L1-L2, L1-L5, and L2-L5) for estimating ionospheric total electron content (TEC) and the rate of TEC (ROT) using Quasi-Zenith Satellite System (QZSS) observations over the Korean Peninsula. GNSS data collected from nine stations across the Korean Peninsula were analyzed for the period from Day of Year (DOY) 1 to 182 in 2024. Differential Code Bias (DCB) was estimated for QZSS satellites, showing high temporal stability with daily variations within ±0.3 ns. The TEC values derived from three different signal combinations were compared with the CODE Global Ionospheric Map (GIM). Compared to other combinations, the L1-L5 pair shows the closest agreement with the CODE GIM, yielding a mean bias of +0.25 TEC units (TECU) with a root mean square (RMS) of 3.59 TECU. In addition, the ROT analysis over the consecutive six days revealed that the L1-L5 combination consistently exhibited the lowest RMS values of about 0.027 TECU compared to other signal pairs. As a result, we suggest that the L1-L5 combination can provide better performance for QZSS-based ionospheric monitoring and TEC studies. Full article
(This article belongs to the Special Issue Advances in GNSS Remote Sensing for Ionosphere Observation)
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24 pages, 5203 KiB  
Article
Insights into Conjugate Hemispheric Ionospheric Disturbances Associated with the Beirut Port Explosion on 4 August 2020 Using Multi Low-Earth-Orbit Satellites
by Adel Fathy, Yuichi Otsuka, Essam Ghamry, Dedalo Marchetti, Rezy Pradipta, Ahmed I. Saad Farid and Mohamed Freeshah
Remote Sens. 2025, 17(11), 1908; https://doi.org/10.3390/rs17111908 - 30 May 2025
Viewed by 242
Abstract
In this study, we analysed remote sensing data collected during the Beirut port explosion on 4 August 2020 at 15.08 UT. For this purpose, we selected three Low-Earth-Orbit (LEO) satellite missions that passed near the Beirut port explosion site immediately after the event. [...] Read more.
In this study, we analysed remote sensing data collected during the Beirut port explosion on 4 August 2020 at 15.08 UT. For this purpose, we selected three Low-Earth-Orbit (LEO) satellite missions that passed near the Beirut port explosion site immediately after the event. The satellites involved were Swarm-B, the Defence Meteorological Satellite Program (DMSP-F17), and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2). This study focused on identifying the possible ionospheric signatures of explosion in both hemispheres. The conjugate hemispheric points were traced using the International Geomagnetic Reference Field (IGRF) model. We found that the satellite data revealed disturbances not only over the explosion site in the Northern Hemisphere, but also in its corresponding conjugate region in the Southern Hemisphere. Ionospheric electron density disturbances were observed poleward in the conjugate hemispheres along the paths of the Swarm and DMSP satellites, whereas the magnetic field data from Swarm-B showed both equatorward and poleward disturbances. Additionally, the ionospheric disturbances detected by Swarm-B (18:52 UT) and DMSP-F17 (16:30 UT) at the same location suggested travelling ionospheric disturbance (TID) oscillations with identical spatial patterns for both satellites, whereas the disturbances observed by COSMIC-2 south of the explosion site (10°N) indicated the radial propagation of TIDs. COSMIC-2 not only recorded equatorward topside (>550 km) ionospheric electron density disturbances, but also in the conjugate hemispheres, which aligns with the time frame reported in previous studies. These ionospheric features observed by multiple LEO satellites indicate that the detected signatures originated from the event, highlighting the importance of integrating space missions for monitoring and gaining deeper insight into space hazards. The absence of equatorward ionospheric disturbances at the altitudes of DMSP-F17 and Swarm-B warrant further investigation. Full article
(This article belongs to the Special Issue Advances in GNSS Remote Sensing for Ionosphere Observation)
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17 pages, 17812 KiB  
Article
Multi-Instrument Analysis of Ionospheric Equatorial Plasma Bubbles over the Indian and Southeast Asian Longitudes During the 19–20 April 2024 Geomagnetic Storm
by Sampad Kumar Panda, Siva Sai Kumar Rajana, Chiranjeevi G. Vivek, Jyothi Ravi Kiran Kumar Dabbakuti, Wangshimenla Jamir and Punyawi Jamjareegulgarn
Remote Sens. 2025, 17(6), 1100; https://doi.org/10.3390/rs17061100 - 20 Mar 2025
Viewed by 1355
Abstract
In this study, we explored the occurrence of near-sunrise equatorial plasma bubbles (EPBs) and inhibition of dusk-time EPBs during the geomagnetic storm (SYM-Hmin= −139 nT) of 19–20 April 2024 using multi-instrument observations over the Indian and Southeast Asian longitude sectors. The initial phase [...] Read more.
In this study, we explored the occurrence of near-sunrise equatorial plasma bubbles (EPBs) and inhibition of dusk-time EPBs during the geomagnetic storm (SYM-Hmin= −139 nT) of 19–20 April 2024 using multi-instrument observations over the Indian and Southeast Asian longitude sectors. The initial phase of this storm commenced around 0530 UT on 19 April 2024 and did not manifest any visible alterations in the ionospheric electric fields during the main phase of the storm, which corresponded to a period between post-sunset to midnight over the study region. However, during the recovery phase of the storm, the IMF Bz suddenly flipped northward and was associated with an overshielding of the penetrating electric fields, which triggered the formation of near-sunrise EPBs. Interestingly, the persistence of EPBs was also noticed for more than three hours after the sunrise terminator. Initially, sunrise EPBs were developed in the Southeast Asian region and later drifted toward the Indian longitude region, along with the sunrise terminator. Moreover, this study suggested that the occurrence of EPBs was suppressed due to the altered storm time electric fields at the dip equatorial region across the 70–90°E longitude sector in the recovery period. This study highlighted that even moderate geomagnetic storms can generate near-sunrise EPBs in a broader longitude sector due to penetrating electric fields in overshielding conditions, which can significantly affect trans-ionospheric signals. Full article
(This article belongs to the Special Issue Advances in GNSS Remote Sensing for Ionosphere Observation)
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Review

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27 pages, 39507 KiB  
Review
Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities
by Renzhong Zhang, Haorui Li, Yunxiao Shen, Jiayi Yang, Wang Li, Dongsheng Zhao and Andong Hu
Remote Sens. 2025, 17(1), 124; https://doi.org/10.3390/rs17010124 - 2 Jan 2025
Cited by 5 | Viewed by 5266
Abstract
With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. [...] Read more.
With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research. Full article
(This article belongs to the Special Issue Advances in GNSS Remote Sensing for Ionosphere Observation)
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