IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Aims & Scope

The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society.

The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations.

The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program.

Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries.

Some of these are areas not traditionally addressed in the IEEE context.

These include biodiversity, health and climate.

Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS.

Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Earth and Planetary Sciences, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Earth and Planetary Sciences, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 1,496.00 USD. Learn more.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

  Citation: 2357

  Authors: José M., Antonio, Nicolas, Mario, Qian, Paul, Jocelyn

• Deep Learning-Based Classification of Hyperspectral Data

  Citation: 2283

  Authors: Yushi, Zhouhan, Xing, Gang, Yanfeng

• EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

  Citation: 1186

  Authors: Patrick, Benjamin, Andreas, Damian

• Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

  Citation: 1087

  Authors: Yushi, Xing, Xiuping

• Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

  Citation: 813

  Authors: Mohammadreza, Masoud, Hamid, Fariba, Pedram, Saeid

• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

  Citation: 793

  Authors: Gong, Xingxing, Junwei, Lei, Gui-Song

• Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

  Citation: 785

  Authors: Meisam, Arsalan, Seyed Ali, Mohammad, Armin, S. Mohammad, Sayyed Hamed Alizadeh, Sahel, Masoud, Saeid, Qiusheng, Brian

• Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems

  Citation: 566

  Authors: Elke, Johannes, Detlev, Hans Georg

• DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

  Citation: 554

  Authors: Jie, Ziyang, Jian, Li, Haozhe, Jiawei, Yu, Haifeng

• A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening

  Citation: 533

  Authors: Qiangqiang, Yancong, Xiangchao, Huanfeng, Liangpei