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Satellite Observations and Numerical Studies for Ionosphere, Plasma Dynamics, and Space Weather Prediction

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 256

Special Issue Editors


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Guest Editor
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: ionosphere; satellite observation; atmospheric physics; space weather

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Guest Editor
Satellite Technology Research Center, KAIST (Korea Advanced Institute of Science and Technology), Daejeon 34141, Republic of Korea
Interests: plasma instruments for ionospheric observation; ionospheric data analysis; preseismic disturbance of ionosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ionospheric disturbances are considered one of the largest error sources in satellite navigation systems and satellite communications. With the development of technology, the ionospheric and plasma characteristics can now be accurately observed via satellite measurement, which provides an effective way for us to deepen our knowledge of near-Earth space and to better understand the impact of space weather events on precise positioning utilized for navigation.

This Special Issue aims to document the use of satellite remote sensing and in situ measurements to characterize ionospheric and plasma dynamics in the near-Earth environment and asses their impact on space weather.

Topics include, but are not limited to, the following:

- Multi-instrument ionospheric observations;
- The impact of sunlit, solar, and geomagnetic activity on the ionosphere at all latitudes;
- Magnetosphere–ionosphere–thermosphere coupling;
- Ionospheric response to geomagnetic storms;
- Accuracy and precision of radio occultation data;
- Space weather and numerical weather prediction;
- Global ionosphere/plasma through GNSS or low earth orbit satellites;
- Interaction mechanism between ionospheric disturbance/delay and space weather;
- Retrieval and processing techniques;
- AI-driven prediction and modeling of relevant ionospheric parameters;
- ESA's three-satellite Swarm mission;
- CSES (China Seismo-Electromagnetic Satellite);
- C/NOFS (Communications/Navigation Outage Forecasting System);
- ICON (Ionospheric Connection Explorer).

Dr. Dehe Yang
Prof. Dr. Kwangsun Ryu
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

  • ionosphere
  • geomagnetic activities
  • space plasma
  • space weather/numerical weather monitoring
  • satellite remote sensing
  • in situ measurements
  • radio occultation

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Published Papers (1 paper)

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Research

21 pages, 5234 KiB  
Article
Revolutionizing the Detection of Lightning-Generated Whistlers: A Rapid Recognition Model with Parallel Bidirectional SRU Network
by Bolin Wang, Jing Yuan, Dehe Yang, Zhihong Zhang, Hanke Yin, Qiao Wang, Jie Wang, Zeren Zhima and Xuhui Shen
Remote Sens. 2025, 17(12), 1963; https://doi.org/10.3390/rs17121963 - 6 Jun 2025
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Abstract
Lightning-generated whistlers (LW) play a crucial role in understanding magnetosphere–ionosphere coupling mechanisms and, perhaps, identifying precursor signals of natural disasters, such as volcanic eruptions and earthquakes. Traditional frequency–time image recognition techniques require approximately 40 years to analyze seven years of observational data from [...] Read more.
Lightning-generated whistlers (LW) play a crucial role in understanding magnetosphere–ionosphere coupling mechanisms and, perhaps, identifying precursor signals of natural disasters, such as volcanic eruptions and earthquakes. Traditional frequency–time image recognition techniques require approximately 40 years to analyze seven years of observational data from the China Seismo-Electromagnetic Satellite (CSES), which fails to meet the requirements for practical implementation. To address this issue, a novel and highly efficient model for LW recognition is proposed, integrating speech processing technology with a parallel bidirectional Simple Recurrent Unit (SRU) neural network. The proposed model significantly outperforms traditional methods in computational efficiency, reducing the parameter count by 99% to 0.1 M and enhancing processing speed by 99%, achieving 20 ms per sample. Despite these improvements, the model maintains excellent performance metrics, including 93% precision, 88.7% recall, and 90.7% F1-score, which is a measure of predictive performance. As a result, the model can process seven years of data in just 33 days, marking a 442-fold increase in processing speed compared to conventional approaches. Full article
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