IEEE Geoscience and Remote Sensing Letters

Aims & Scope

IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts.

Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information.

The technical content of papers must be both new and significant.

Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions.

GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	4.4

SJR (SCImago Journal Rank)

Year	Value
2024	1.258

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	163

Journal Rank

Year	Value
2024	3258

Journal Citation Indicator

Year	Value
2024	16671

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Abstracting & Indexing

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

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

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

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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.

• Road Extraction by Deep Residual U-Net

  Citation: 2398

  Authors: Zhengxin, Qingjie, Yunhong

• HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification

  Citation: 1439

  Authors: Swalpa Kumar, Gopal, Shiv Ram, Bidyut B.

• Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

  Citation: 1410

  Authors: Nataliia, Mykola, Sergii, Andrii

• A Landsat Surface Reflectance Dataset for North America, 1990–2000

  Citation: 1352

  Authors: J.G., E.F., N.E., R., F.G., K.F., F., J., T.-K.

• Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering

  Citation: 948

  Authors:

• Composite Kernels for Hyperspectral Image Classification

  Citation: 944

  Authors: G., L., J., J., J.

• Deep Learning Based Feature Selection for Remote Sensing Scene Classification

  Citation: 788

  Authors: Qin, Lihao, Tong, Qian

• Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data

  Citation: 741

  Authors: Juan C., Jose A., Drazen, Cristian, Jordi

• SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images

  Citation: 674

  Authors: Yuliya, Mathieu, Jocelyn, Jón Atli

• SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images

  Citation: 648

  Authors: Sheng, Kaiyu, Jinyuan, Zhe

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