Techniques and Science Exploitations for Earth Observation and Planetary Exploration-2nd Edition

Topic Information

Dear Colleagues,

In recent decades, satellite missions have dramatically improved our understanding of Earth and the planets within the Solar System. Satellite observations generate vast amounts of data, enabling numerous scientific discoveries and technological innovations. However, this extensive data remains significantly underexploited, mainly due to limitations in traditional data processing methods and computational capacity.

Artificial intelligence (AI), particularly machine learning and deep learning techniques, is transforming Earth Observation (EO) and planetary science. AI methods allow for rapid analysis and interpretation of large satellite datasets, improving accuracy and opening up new research avenues. Examples include advanced data enhancement, feature detection, scene classification, dynamic feature tracking, and AI-driven topographic mapping. The availability and curation of high-quality training datasets is also a critical area of development, ensuring robust model performance and generalisability across instruments, platforms, applications, and planetary environments.

Beyond AI, recent developments across EO and planetary missions have advanced our capabilities in sensor design, data acquisition, and scientific exploitation. For Earth Observation, multi-spectral and hyperspectral instruments, synthetic aperture radar (SAR), thermal sensors, and altimeters provide continuous and multi-dimensional datasets for environmental monitoring, climate science, disaster response, land use mapping, and urban planning. Multi-mission data fusion and long-term series analysis have become essential tools for understanding Earth system dynamics and global change.

In planetary science, orbiters and landers have enabled in-depth studies of the Moon, Mars, and other planetary bodies. High-resolution imaging systems, spectrometers, and topographic sensors have been used to map geological features, assess mineral compositions, and investigate geomorphological activity and surface evolution. These datasets support ongoing efforts in automated terrain classification, landing site selection, and scientific target identification. The integration of EO techniques and EO-derived planetary datasets with ground-based or in situ observations is also an emerging area of interest.

The first volume of this Topic collated 43 high-quality papers and provided clear evidence of the fast growth of the satellite remote sensing community. We are pleased to announce the release of Volume II of the Topic “Satellite Missions, Techniques and Science Exploitations for Earth Observation and Planetary Exploration” and invite contributions from the broader EO and planetary communities, focusing on the following:

• Novel AI-driven techniques for satellite data enhancement and interpretation;
• Machine learning and deep learning applications in EO and planetary remote sensing;
• Creation, annotation, and benchmarking of training datasets for AI model development;
• Studies of geological and geographical features using satellite data;
• Change detection, time-series analysis, and dynamic monitoring using multi-temporal satellite data;
• Radar and multispectral image processing for land, ocean, and atmospheric studies;
• Fusion of data from different sensors and missions to improve spatial and temporal coverage;
• AI-driven autonomous navigation and data analysis for planetary rovers and landers;
• Three-dimensional terrain reconstruction and topographic mapping from stereo and monocular satellite imagery;
• Onboard and edge processing for real-time or resource-constrained mission environments;
• Mission concepts, payload development, and scientific results from past and upcoming EO and planetary missions.

We welcome original research articles, comprehensive reviews, data descriptors, and detailed case studies that address the above and related topics. Submissions will undergo prompt and rigorous peer review to ensure timely publication. We look forward to receiving your valuable contributions. 

Dr. Yu Tao
Dr. Siting Xiong
Dr. Rui Song
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.2	4.0	2014	22.9 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Data data	2.0	5.0	2016	25 Days	CHF 1600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Universe universe	2.6	5.2	2015	21.8 Days	CHF 1600	Submit

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Published Papers (4 papers)

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All Aerospace Applied Sciences Data Remote Sensing Sensors Universe

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21 pages, 4199 KB  

Open AccessArticle

Using Electrodynamic Tethers to Create Artificial Sun-Synchronous Orbits and De-Orbit Remote Sensing Satellites

by Antonio F. B. A. Prado and Vladimir Razoumny

Universe 2026, 12(4), 102; https://doi.org/10.3390/universe12040102 - 2 Apr 2026

Viewed by 409

Abstract

This paper has the goal of exploring the potential of electromagnetic propulsion systems based on tethers to create artificial Sun-synchronous orbits for remote sensing satellites, as well as performing station-keeping maneuvers and de-orbiting of the satellite after the end of its useful life. [...] Read more.

This paper has the goal of exploring the potential of electromagnetic propulsion systems based on tethers to create artificial Sun-synchronous orbits for remote sensing satellites, as well as performing station-keeping maneuvers and de-orbiting of the satellite after the end of its useful life. To create artificial Sun-synchronous orbits, the force is applied to keep the longitude of the ascending node with the same angular velocity of the apparent motion of the Sun around the Earth, which is the definition of a Sun-synchronous orbit. These orbits are very important for remote sensing satellites, because in these orbits the satellite passes by a given point at the same time, helping in analyzing the data collected. The use of electrodynamic tethers can extend the regions of Sun-synchronous orbits, both in terms of inclination and semi-major axis. To perform the de-orbiting of the satellite, the same tether can apply a force in the opposite direction of the motion of the satellite, so reducing its energy and decreasing the semi-major axis until the satellite crashes into the atmosphere of the Earth. This is very important to avoid increasing the presence of space debris in space, a very serious problem nowadays. For the station-keeping maneuvers, we just need to use the appropriate control laws, from time to time, to correct any errors in the Keplerian elements. A significant advantage of employing an electrodynamic tether over traditional thrusters is that it does not require consumption of fuel. The study assumes that a current can flow in both directions through the tether, so interacting with the magnetic field of the Earth to create the Lorentz force. The possibility of using electrodynamic tethers with autonomous charge generation, to avoid dependence on plasma densities and other external factors, is considered. The results presented here help in space and planetary science, since they give more options for remote sensing satellites, which are a key element in planetary science. Full article

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration-2nd Edition)

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33 pages, 2581 KB  

Open AccessReview

Regulatory and Spectrum Challenges for Passive Space Weather Monitoring

by Valeria Leite, Tarcisio Bakaus, Mateus Cardoso, Marco Antonio Bockoski de Paula and Alison Moraes

Universe 2026, 12(3), 74; https://doi.org/10.3390/universe12030074 - 5 Mar 2026

Cited by 1 | Viewed by 408

Abstract

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision [...] Read more.

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration-2nd Edition)

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26 pages, 4116 KB  

Open AccessArticle

U-Net Based Forecasting of Storm-Time Total Electron Content over North Africa Using Assimilation of GNSS Observation into Global Ionospheric Maps

by Adel Fathy, Ahmed. I. Saad Farid, Daniel Okoh, Patrick Mungufeni, Ayman Mahrous, Mohamed Nassar, Yuichi Otsuka, Weizheng Fu, John Bosco Habarulema, Haitham El-Husseiny and Ahmed Arafa

Universe 2026, 12(2), 54; https://doi.org/10.3390/universe12020054 - 18 Feb 2026

Cited by 1 | Viewed by 737

Abstract

This study presents U-Net deep learning of total electron content (TEC) obtained from Global Ionosphere Maps (GIMs) to forecast ionospheric TEC over the African 0–40° N latitude sector during geomagnetic storms which have occurred between 2011 and 2024. Before being utilized in the [...] Read more.

This study presents U-Net deep learning of total electron content (TEC) obtained from Global Ionosphere Maps (GIMs) to forecast ionospheric TEC over the African 0–40° N latitude sector during geomagnetic storms which have occurred between 2011 and 2024. Before being utilized in the deep learning procedure, the GIM-TEC data were improved by assimilating ground-based vertical TEC (VTEC) observations from available Global Navigation Satellite System (GNSS) receiver stations. The U-Net one-hour-ahead prediction of TEC was examined during the intense geomagnetic storm of May 2024. Additionally, the model’s accuracy and reliability were evaluated through quantitative comparison with established climatological models, including IRI-2020 and AfriTEC storm time models. The results indicate that the integration of data assimilation with the deep learning framework yields TEC estimates that closely agree with observations, achieving a RMSE of approximately 5 TECU. On the other hand, the IRI-2020 model exhibits substantially larger errors, with RMSE ~10–17 TECU, while the AfriTEC model shows the poorest performance, with RMSE reaching approximately 15–22 TECU. Further, the U-Net was validated using two equatorial and mid-latitude GNSS stations whose data were excluded from the assimilation process, achieving RMSE values of 4.44 and 6.75 TECU and correlation coefficients of 0.93 and 0.97, confirming the model forecasting capability for reproducing ionospheric TEC variability. These results establish the model as a precise, robust tool for TEC prediction in regions with sparse GPS coverage that is crucial for ionospheric monitoring and space weather applications. Full article

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration-2nd Edition)

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17 pages, 5089 KB  

Open AccessArticle

Monitoring and Analysis of Land Subsidence Induced by Social Aggregation Effects for Operational Subway via PS-InSAR: A Case Study in Guangzhou Metro Line 6, China

by Jingxin Hou, Yang Liu, Zeying Lan, Xing Min, Xiao Zhang, Guochao Liu, Chunshuai Si and Yanan Du

Appl. Sci. 2025, 15(21), 11492; https://doi.org/10.3390/app152111492 - 28 Oct 2025

Viewed by 1153

Abstract

With the continuous construction and operation of urban subways, rapid changes in various urban elements have occurred, subsequently resulting in land subsidence along subway lines. Compared to the construction period, monitoring and multi-factor analysis of subway deformation during the operational period is relatively [...] Read more.

With the continuous construction and operation of urban subways, rapid changes in various urban elements have occurred, subsequently resulting in land subsidence along subway lines. Compared to the construction period, monitoring and multi-factor analysis of subway deformation during the operational period is relatively limited. In this paper, we examine the issue through the novel lens of socio factor agglomeration. Both Sentinel-1, TerraSAR-X, ascending/descending LuTan-1 images and a Block PS-InSAR method were used to monitor 8-year ground subsidence for Kemulang station on Guangzhou Metro Line 6. Compared with the leveling measurements, the root mean square error (RMSE) of the InSAR results was 2.24 mm. Furthermore, social agglomeration effects such as population concentration, property clustering, commercial aggregation and the intensification of resource consumption were considered to analyze the main reason of ground subsidence, the synergistic process of multiple factors and the mechanism of accelerated subsidence phenomenon. We can find from the results that the fundamental cause of the large-scale land subsidence along the subway line is groundwater over-extraction triggered by population agglomeration, coupled with the response of adverse geological formations. Groundwater over-extraction has caused irreversible damage to the local strata. The research shows that the social agglomeration effect will cause more complex disturbance to the subway and lead to more continuous ground subsidence and more covert safety threat for subway operation, which should not be ignored. Full article

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration-2nd Edition)

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