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	20.9 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Data data	2.0	5.0	2016	25.2 Days	CHF 1600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Universe universe	2.6	5.2	2015	22.6 Days	CHF 1600	Submit

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