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Remote Sensing
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Ionosphere and Space Weather Based on Satellite Remote Sensing Observation
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Ionosphere and Space Weather Based on Satellite Remote Sensing Observation

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 3133

Special Issue Editor

Prof. Dr. Xudong Gu

E-Mail Website
Guest Editor
School of Earth and Space Science and Technology, Wuhan University, Wuhan 430079, China
Interests: magnetospheric physics; interplanetary physics; space weather modeling; extremely/very low-frequency wave detection system; exceptionally low-frequency wave propagation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ionosphere is a dynamic region of Earth's upper atmosphere that plays a vital role in space weather processes, influencing satellite communications, GNSS positioning, and radar systems. Space weather events such as solar flares, coronal mass ejections, and geomagnetic storms can cause significant ionospheric disturbances, leading to communication blackouts, signal degradation, and navigation errors. Understanding and forecasting these disturbances is essential for the resilience of modern technological infrastructure. Satellite remote sensing has become an indispensable tool for observing the ionosphere, offering global coverage, high temporal resolution, and the ability to monitor real-time ionospheric variability. When integrated with ground-based observations—such as GNSS networks, ionosondes, and incoherent scatter radars—these datasets enable a multi-scale view of ionospheric dynamics. Recent advances in remote sensing technologies, data fusion techniques, and physics-informed models, including those based on machine learning, are driving innovation in ionospheric and space weather research. This research area is critically important for enhancing our scientific understanding and operational capability in space weather monitoring and prediction. As the dependency on satellite-based systems grows, so does the urgency for accurate, timely, and global-scale assessments of ionospheric behavior.

This Special Issue aims to highlight cutting-edge developments in remote sensing technologies and methodologies for ionospheric and space weather research. A particular focus will be placed on innovative approaches that leverage the synergy between satellite and ground-based observations, and on modeling and forecasting techniques—including those utilizing machine learning—that contribute to space weather resilience. In line with Remote Sensing's mission, this Issue seeks contributions that address atmospheric and environmental challenges using advanced remote sensing techniques. We especially welcome studies that push the boundaries of observational capabilities or predictive modeling in the context of ionospheric disturbances and space weather impacts.

We invite original research articles, technical notes, and comprehensive reviews in the following areas:

  1. Innovative satellite-based techniques for ionospheric monitoring and their integration with ground-based systems.
  2. Detection and characterization of ionospheric disturbances (e.g., TIDs, scintillation, storm-time effects) via remote sensing.
  3. Data assimilation and space weather forecasting models, including AI/ML-based approaches, using multi-source observations.
  4. Impact assessment of ionospheric disturbances on GNSS, satellite communications, and radar operations.
  5. New satellite missions, sensor technologies, or data fusion frameworks for improved space weather resilience.
  6. Cross-disciplinary approaches combining remote sensing, geophysics, and atmospheric modeling to address space environment challenges.

This Special Issue encourages both technological innovations and modeling advancements, particularly those with practical applications in forecasting and early warning systems.

Prof. Dr. Xudong Gu
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • ionospheric monitoring
  • space weather impacts
  • satellite-based observations
  • ionospheric irregularities
  • GNSS disruptions
  • remote sensing for space weather
  • ionospheric scintillation
  • space weather forecasting models

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Published Papers (3 papers)

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19 pages, 10558 KB  
Article
Ionospheric Disturbances from the 2022 Hunga-Tonga Volcanic Eruption: Impacts on TEC Spatial Gradients and GNSS Positioning Accuracy Across the Japan Region
by Zhihao Fu, Xuhui Shen, Qinqin Liu and Ningbo Wang
Remote Sens. 2025, 17(17), 3108; https://doi.org/10.3390/rs17173108 - 6 Sep 2025
Cited by 1 | Viewed by 1185
Abstract
The Hunga-Tonga volcanic eruption on 15 January 2022, produced significant atmospheric and ionospheric disturbances that may degrade global navigation satellite system (GNSS) and precise point positioning (PPP) accuracy. Using data from the GEONET GNSS network and Soratena barometric pressure sensors across Japan, we [...] Read more.
The Hunga-Tonga volcanic eruption on 15 January 2022, produced significant atmospheric and ionospheric disturbances that may degrade global navigation satellite system (GNSS) and precise point positioning (PPP) accuracy. Using data from the GEONET GNSS network and Soratena barometric pressure sensors across Japan, we analyzed the eruption’s effects through the gradient ionospheric index (GIX) and the rate of TEC index (ROTI) to characterize the propagation and effects of these disturbances on ionospheric total electron content (TEC) gradients. Our analysis identified two separate ionospheric disturbance events. The first event, coinciding with the arrival of atmospheric Lamb waves, was characterized by wave-like pressure anomalies, differential TEC (dTEC) fluctuations, and modest horizontal gradients of vertical TEC (VTEC). In contrast, the second, more pronounced disturbance was driven by equatorial plasma bubbles (EPBs), which generated severe ionospheric irregularities and large TEC gradients. Further analysis revealed that these two disturbances had markedly different impacts on GNSS positioning accuracy. The Lamb wave–induced disturbance mainly caused moderate TEC fluctuations with limited effects on positioning accuracy, and mid-latitude stations maintained both average and 95th percentile positioning (ppp,P95) errors below 0.1 m throughout the event. In contrast, the EPB-driven disturbance had a substantial impact on low-latitude regions, where the average horizontal PPP error peaked at 0.5 m and the horizontal and vertical ppp,P95 errors exceeded 1 m. Our findings reveal two episodes of spatial-gradient enhancement and successfully estimate the propagation speed and direction of the Lamb waves, supporting the potential application of ionospheric gradient monitoring in forecasting GNSS performance degradation. Full article
(This article belongs to the Special Issue Ionosphere and Space Weather Based on Satellite Remote Sensing Observation)
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22 pages, 6640 KB  
Article
IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions
by Mert Can Turkmen, Yee Hui Lee and Eng Leong Tan
Remote Sens. 2025, 17(15), 2557; https://doi.org/10.3390/rs17152557 - 23 Jul 2025
Cited by 2 | Viewed by 743
Abstract
Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step [...] Read more.
Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step toward fostering rigorous and reproducible evaluation of AI models for ionospheric forecasting by introducing IonoBench: a benchmarking framework that employs a stratified data split, balancing solar intensity across subsets while preserving 16 high-impact geomagnetic storms (Dst ≤ −100 nT) for targeted stress testing. Using this framework, we benchmark a field-specific model (DCNN) against state-of-the-art spatiotemporal architectures (SwinLSTM and SimVPv2) using the climatological IRI 2020 model as a baseline reference. DCNN, though effective under quiet conditions, exhibits significant degradation during elevated solar and storm activity. SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to 32.1% across various subsets under diverse solar conditions. The reported results highlight the value of cross-domain architectural transfer and comprehensive evaluation frameworks in ionospheric modeling. With IonoBench, we aim to provide an open-source foundation for reproducible comparisons, supporting more meticulous model evaluation and helping to bridge the gap between ionospheric research and modern spatiotemporal deep learning. Full article
(This article belongs to the Special Issue Ionosphere and Space Weather Based on Satellite Remote Sensing Observation)
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14 pages, 3376 KB  
Technical Note
Ionospheric TEC Forecasting with ED-ConvLSTM-Res Integrating Multi-Channel Features
by Jiayue Yang, Wengeng Huang, Lei Zhang, Heng Xu, Hua Shen, Xin Wang and Ming Li
Remote Sens. 2025, 17(21), 3564; https://doi.org/10.3390/rs17213564 - 28 Oct 2025
Viewed by 615
Abstract
This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder [...] Read more.
This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder (ED) architecture enhanced with residual connections and convolutional channel projection, which collectively improve the synergy among its core components. Based on this framework, we developed ED-ConvLSTM-Res, a multi-channel feature-based global ionospheric TEC prediction model. Comprehensive accuracy evaluation and comparative tests were carried out using datasets from the solar minimum year of 2019 and the current solar maximum year of 2024. The results indicate that the proposed model consistently achieves strong predictive performance compared with other models, along with a significantly enhanced feature representation capability. Specifically, the Root Mean Square Errors (RMSE) of the ED-ConvLSTM-Res model’s predictions in 2019 and 2024 are 1.28 TECU and 5.28 TECU, respectively, while the corresponding Mean Absolute Errors (MAE) are 0.87 and 3.87, and the coefficients of determination (R2) are 0.95 and 0.94. In the current high solar activity year 2024, the proposed model achieves error reductions of 13.6% in MAE and 11.6% in RMSE compared with the Center for Orbit Determination in Europe (CODE)’s one-day-ahead forecast product, c1pg. These results confirm that the proposed model not only outperforms the ConvLSTM model without additional indices and c1pg but also exhibits strong generalization capability, maintaining stable performance with low errors under both high and low solar activity conditions. Full article
(This article belongs to the Special Issue Ionosphere and Space Weather Based on Satellite Remote Sensing Observation)
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