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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: 30 June 2025 | Viewed by 5589

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 policies can be found here.

Related Special Issue

Published Papers (6 papers)

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Research

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17 pages, 16750 KiB  
Article
Nighttime Tweek Characteristics in Mid–Low Latitudes: Insights from Long-Term VLF Observations in China
by Qingshan Wang, Binbin Ni, Jingyuan Feng, Xudong Gu, Wei Xu, Shiwei Wang, Mengyao Hu, Wenchen Ma, Wen Cheng, Yufeng Wu and Junjie Zhang
Remote Sens. 2025, 17(3), 438; https://doi.org/10.3390/rs17030438 - 27 Jan 2025
Cited by 1 | Viewed by 533
Abstract
An improved method for identifying nighttime tweek signals in WHU VLF measurements was developed by redesigning the extraction process and validated through comparison with World-Wide Lightning Location Network (WWLLN) data. Using the enhanced method, 1,728,032 tweek signals were identified from four years (2018–2021) [...] Read more.
An improved method for identifying nighttime tweek signals in WHU VLF measurements was developed by redesigning the extraction process and validated through comparison with World-Wide Lightning Location Network (WWLLN) data. Using the enhanced method, 1,728,032 tweek signals were identified from four years (2018–2021) of VLF data, forming the most comprehensive tweek dataset for the mid–low latitude region in China. Statistical analysis reveals distinct nighttime variations in tweek occurrence rates, which increase from 18:00 LT to 20:00 LT, remain high until 04:00 LT, and gradually decrease towards sunrise. Seasonal differences in propagation distance are evident, ranging from ~2000 km in summer to ~4000 km in winter, corresponding to the seasonal shift of lightning activity. The cutoff frequency showed apparent daily and seasonal fluctuations, and the trends of daily variation are opposite between winter and summer. The annual variation in cutoff frequency presents a pattern different from previous cognition, with a minimum of 1.62 kHz in summer and a maximum of 1.68 kHz in winter, influenced by the magnetic cyclotron frequency at ionospheric reflection points. These findings improve the understanding of nighttime tweek characteristics and ionospheric dynamics in East Asia, offering valuable insights for ionospheric research and VLF communication systems. 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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20 pages, 5665 KiB  
Article
Impact of Solar Wind Dynamic Pressure on Polar Electrojets and Large- and Small-Scale Field-Aligned Currents
by Hui Wang and Zhiyue Leng
Remote Sens. 2025, 17(3), 427; https://doi.org/10.3390/rs17030427 - 27 Jan 2025
Viewed by 601
Abstract
This study examines the impact of the solar wind dynamic pressure (Pd) on the peak current density and latitude of polar electrojets (PEJs), large-scale field-aligned currents (LSFACs), and small-scale FACs (SSFACs) in various local times, seasons, and hemispheres, using Swarm observations [...] Read more.
This study examines the impact of the solar wind dynamic pressure (Pd) on the peak current density and latitude of polar electrojets (PEJs), large-scale field-aligned currents (LSFACs), and small-scale FACs (SSFACs) in various local times, seasons, and hemispheres, using Swarm observations during 2014 to 2020. The different Pd effects with enhanced solar wind mass density (Nsw effect) or with enhanced solar wind velocity (Vsw effect) are differentiated. LSFACs and PEJs show pronounced hemispheric and seasonal differences around noontime, where summer variations are more pronounced than winter, due to higher solar EUV conductivity. Increased Pd typically enhances LSFACs, except at midnight when opposing effects from Nsw and Vsw exert on poleward-side FACs. The impact of Vsw on FACp surpasses that of Nsw mostly except for midnight. In contrast, the Nsw impacts on equatorward-side FACs and SSFACs are mostly stronger than the Vsw effect except for the noontime. PEJs strengthen with increasing Vsw effects more efficiently than with increasing Nsw effects. Additionally, a higher Pd shifts PEJs and SSFACs equatorward, with Vsw effects being more prominent than Nsw effects, except for midnight SSFACs. 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, 3024 KiB  
Article
Correlation Study of Auroral Currents with External Parameters During 10–12 October 2024 Superstorm
by Xiaotong Xia, Xue Hu, Hui Wang and Kedeng Zhang
Remote Sens. 2025, 17(3), 394; https://doi.org/10.3390/rs17030394 - 24 Jan 2025
Viewed by 662
Abstract
This study investigated the correlations between field-aligned currents (FACs), polar electrojets (PEJs), and external solar and geomagnetic activity parameters during the intense geomagnetic storm that occurred from 10 to 12 October 2024. Notably, the merging electric field (Em) had a greater impact on [...] Read more.
This study investigated the correlations between field-aligned currents (FACs), polar electrojets (PEJs), and external solar and geomagnetic activity parameters during the intense geomagnetic storm that occurred from 10 to 12 October 2024. Notably, the merging electric field (Em) had a greater impact on FACs and PEJs compared to the May 2024 storm, while the influence of solar wind pressure (Pd) was equally important in both storms. The peak FAC densities in the northern dawn (southern dusk) and nighttime sectors correlate strongly with Em, whereas Pd dominates in the northern dusk (southern dawn) and daytime sectors. For PEJs, Em correlates strongly with current densities in the northern dawn–dusk and southern nighttime sectors, while Pd is the primary correlated parameter on the dayside. FAC (PEJ) latitudes are most strongly influenced by Em (Pd or Dst) on the dawnside–duskside. Additionally, FACs and PEJs are mostly more intense on the dawnside than on the duskside and extend to lower latitudes at dusk than at dawn. Analysis of the May and October 2024 storms reveals that FACs in the summer hemisphere are generally stronger and situated at more poleward latitudes than those in the winter hemisphere. This pattern is largely driven by summer–winter variations in ionospheric conductivity, with some differences also arising from the asymmetric magnetic field geometry between the two hemispheres. 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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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 1086
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
Cited by 3 | Viewed by 1389
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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13 pages, 4561 KiB  
Technical Note
A Method of Arrival Angle Optimization in Single-Station Positioning Based on Statistical Features
by Ting Li, Tongxin Liu, Xuehai Yang, Guobin Yang, Chunhua Jiang and Chongzhe Lao
Remote Sens. 2025, 17(2), 343; https://doi.org/10.3390/rs17020343 - 20 Jan 2025
Viewed by 657
Abstract
Aiming to mitigate the substantial dispersion in arrival angle estimation due to colored and white noise interference, which may seriously affect the accuracy of short-wave single-station positioning, this paper introduces an approach to optimizing angles based on the statistical features. By utilizing the [...] Read more.
Aiming to mitigate the substantial dispersion in arrival angle estimation due to colored and white noise interference, which may seriously affect the accuracy of short-wave single-station positioning, this paper introduces an approach to optimizing angles based on the statistical features. By utilizing the extraction of the main peak area of the probability density distribution of the measured angle, as well as the two-dimensional Gaussian fitting and confidence ellipse bounding, the angle measurement results affected by colored noise interference and the noise points with large deviations can be sequentially filtered out. Combining experimental scenarios and confirmed by actual measurement data, the dispersion of arrival angle estimation results has been significantly constrained, and, correspondingly, the positioning accuracy has also been significantly improved by about 3%. 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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