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Applications of Remote Sensing in Monitoring Ionospheric and Atmospheric Physics (Third Edition)

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1613

Special Issue Editors

Dr. Chunhua Jiang

E-Mail Website
Guest Editor
School of Electronic Information Doctor of Geophysics, Wuhan University, Wuhan, China
Interests: ionospheric physics; ionospheric irregularities; automatic scaling of ionograms; propagation of radio waves in the ionosphere; remote Sensing; planetary ionosphere
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huijun Le

E-Mail Website
Guest Editor
Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
Interests: ionospheric weather; ionospheric modeling; ionospheric data assimilation; ionosphere—thermosphere coupling; planetary ionosphere
Special Issues, Collections and Topics in MDPI journals
Dr. Ercha Aa

E-Mail Website
Guest Editor
MIT Haystack Observatory, Westford, MA 01886, USA
Interests: ionospheric irregularities; ionospheric data assimilation; GNSS and radio occultation; subauroral electrodynamics; ionosphere—thermosphere coupling; geospace storm effects
Special Issues, Collections and Topics in MDPI journals
Dr. Zheng Li

E-Mail Website
Guest Editor
Institute of Space Weather, Nanjing University of Information Science & Technology, No. 219, Ningliu Road, Nanjing 210044, China
Interests: nitric oxide cooling in lower thermosphere; ionosphere and middle atmosphere coupling; thermospheric and ionospheric storms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ionosphere, where atoms and molecules are partly ionized by solar radiation, constitutes a significant part of Earth’s upper atmosphere. The free electrons in the ionosphere can significantly affect the propagation of radio waves. The ionosphere plays a critical role in communications and navigation systems in our daily life. Therefore, developing our understanding of this section of our atmosphere is of great importance for human activities. The ionosphere has strong temporal and spatial variability. Being coupled downward to the lower atmosphere and upward to the magnetosphere, the ionosphere is not only affected by solar activities, but also by lower atmospheric waves and geomagnetic disturbances. The ionosphere is also controlled by photochemical, dynamic, and electrodynamic processes. As a result, there are many open questions in the ionospheric community, such as the day-to-day variation in the ionosphere, ionospheric irregularities, ionospheric longitudinal structure, the forecasting of the ionosphere, ionospheric storms, etc.

The middle and upper atmosphere are located at the end of the solar terrestrial energy transfer chain and play important roles in space science research. The middle and upper atmosphere comprise the passage zone for various spacecrafts and the residence zone for low-orbit spacecrafts. Therefore, the heating and cooling process, the temporal and spatial variability, and the transient structure of the atmosphere at this altitude have significant impacts on the safety and precise orbit entry of spacecrafts.

With the development of modern techniques, many remote sensing methods of the ionosphere and the atmosphere, including ionosondes, radars, radio occultations, GNSS receivers, and airglow observations from the ground and spacecraft, have emerged to assist in further understanding the ionosphere and the atmosphere.

In this Special Issue, we aim to improve the understanding of ionospheric and atmospheric physics by the application of remote sensing to the ionosphere and atmosphere. Both original research and review papers are welcome.

We encourage contributions to topics including, but not limited to, the following:

  • Spatial and temporal distributions in the ionosphere/atmosphere;
  • Ionospheric irregularities;
  • Ionospheric/thermospheric modeling;
  • Ionospheric data assimilation;
  • Ionosphere–thermosphere coupling;
  • Traveling ionospheric/atmospheric disturbances;
  • Remote sensing by radio waves and optical imaging;
  • Ionospheric/thermospheric weather.

This Special Issue is the third edition of this topic.

The first edition: Applications of Remote Sensing in Monitoring Ionospheric Physics and Ionospheric Weather Forecasting.

The second edition: Applications of Remote Sensing in Monitoring Ionospheric and Atmospheric Physics

Dr. Chunhua Jiang
Prof. Dr. Huijun Le
Dr. Ercha Aa
Dr. Zheng Li
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
  • atmosphere
  • ionospheric irregularities
  • ionospheric/thermosphric modeling
  • data assimilation
  • geomagnetic storms
  • radars
  • radio occultations
  • GNSS TEC
  • airglow observations

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 7136 KiB  
Article
A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
by Jian Wang, Han Han and Yafei Shi
Remote Sens. 2024, 16(17), 3247; https://doi.org/10.3390/rs16173247 - 2 Sep 2024
Viewed by 639
Abstract
The ionospheric F2 critical frequency (foF2) is one of the most crucial application parameters in high-frequency communication, detection, and electronic warfare. To improve the accuracy of spatial reconstruction of the ionospheric foF2, we propose a high-accuracy surface (HAS) modeling method. This method converts [...] Read more.
The ionospheric F2 critical frequency (foF2) is one of the most crucial application parameters in high-frequency communication, detection, and electronic warfare. To improve the accuracy of spatial reconstruction of the ionospheric foF2, we propose a high-accuracy surface (HAS) modeling method. This method converts difficult-to-solve differential equations into more manageable algebraic equations using direct difference approximation, significantly reducing algorithm complexity and computational load while exhibiting excellent convergence properties. We used seven stations in Brisbane, Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville, with one station as a validation station and six as training stations (e.g., Brisbane as a validation station and the other stations—Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville—as training stations). The training stations and the HAS method were used to train and reconstruct the validation stations at different solar activity periods, seasons, and local times. The predicted values of the validation stations were compared with the measured values, and the proposed method was analyzed and validated. The reconstruction results show the following. (1) The relative root mean square errors (RRMSEs) of HAS method prediction in different solar activity epochs were 13.67%, 7.74%, and 9.19%, respectively, which are 13.57%, 7.41%, and 6.41% higher than the prediction accuracy of the Kriging method, respectively. (2) In the four seasons, the RRMSEs of the HAS method prediction are 9.27%, 13.1%, 8.81%, and 8.09%, respectively, which are 10.83%, 11.73%, 4.25%, and 12.00% higher than the prediction accuracy of the Kriging method. (c) During the daytime and nighttime, the RRMSEs of HAS method prediction were 9.23% and 11.17%, which were 5.92% and 11.99% higher than the prediction accuracy of the Kriging method, respectively. (d) Under the validation dataset, the average predictive RRMSE of the HAS method was 10.29%, and the average predictive RRMSE of the IRI prediction model was 12.35%, with a 2.06% improvement in the predictive accuracy of the HAS method. In general, the prediction effect of the HAS method was better than that of the Kriging method, thus verifying the effectiveness and reliability of the proposed method. In summary, the proposed reconstruction method is of great significance for improving usable frequency prediction and enhancing communication performance. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Monitoring Ionospheric and Atmospheric Physics (Third Edition))
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18 pages, 5877 KiB  
Article
Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method
by Xiaochen Ren, Biqiang Zhao, Zhipeng Ren and Bo Xiong
Remote Sens. 2024, 16(17), 3160; https://doi.org/10.3390/rs16173160 - 27 Aug 2024
Viewed by 650
Abstract
Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) [...] Read more.
Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Monitoring Ionospheric and Atmospheric Physics (Third Edition))
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