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IEEE Geoscience and Remote Sensing Letters

IEEE - Geoscience and Remote Sensing Society (United States)

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IEEE Geoscience and Remote Sensing Letters (GRSL) publishes 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.

To make it convenient to share data and code of an article, IEEE offers author tools such as IEEE DataPort and Code Ocean.

Earth sciences

Geomatics, remote sensing

Open access

Author-paid optional open access

Self-archiving

Sherpa Romeo

Publication languages

English

Journal reputation

Other titles

GRSL

Abbreviated title (ISO)

IEEE Geosci. Remote Sens. Lett.

ISSN L

1545-598X

Frequency

Continual

Article types

Short articles

Publishing costs

Cost of optional open access

2345 $. Corresponding authors from low-income countries are eligible for waived or reduced open access APCs. (updated 22/02/2024)

Research data access policy

Deposit recommended

Data repositories recommended by the journal

https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/tools-for-ieee-authors/#data-code

Toward spatio-spectral analysis of sentinel-2 time series data for land cover mapping. Gbodjo J.E., Ienco D., Leroux L.. 2020. IEEE Geoscience and Remote Sensing Letters, 17(2) DOI : 10.1109/LGRS.2019.2917788

Image-based time series representations for pixelwise eucalyptus region classification: A comparative study. Dias D., Dias U., Menini N., Lamparelli R.A.C., Le Maire G., da S. Torres R.. 2020. IEEE Geoscience and Remote Sensing Letters, 17(8) DOI : 10.1109/LGRS.2019.2946951

A soft computing framework for image classification based on recurrence plots. Menini N., Almeida A.E., Lamparelli R.A.C., Le Maire G., dos Santos J.A., Pedrini H., Hirota M., Torres R.D.S.. 2019. IEEE Geoscience and Remote Sensing Letters, 16(2) DOI : 10.1109/LGRS.2018.2872132

Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR sentinel-1. Ho Tong Minh D., Ienco D., Gaetano R., Lalande N., Ndikumana E., Osman F., Maurel P.. 2018. IEEE Geoscience and Remote Sensing Letters, 15(3) DOI : 10.1109/LGRS.2018.2794581

Land cover classification via multitemporal spatial data by deep recurrent neural networks. Ienco D., Gaetano R., Dupaquier C., Maurel P.. 2017. IEEE Geoscience and Remote Sensing Letters, 14(10) DOI : 10.1109/LGRS.2017.2728698

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Last updated : 28/02/2024

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