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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 7045

Special Issue Editors

Dr. Wang Li

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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

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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

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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

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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 (2 papers)

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Research

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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 1227
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 4 | Viewed by 5082
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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