专刊邀稿|RSE：Advancing deep learning for time series analysis

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Remote sensing time series analysis has been widely used for land cover/use change monitoring and surface parameter inversion. Deep learning models offer operational and practical advantages but should respect remote sensing signal characteristics and application domain pressing challenges. Deep learning for surface parameter estimation that traditionally relied on physical models should be geographically robust, respect physical laws, and/or enable knowledge discovery. Such enhanced deep learning algorithms could substantially advance multiple important disciplines such as land cover studies, time series classification, change detection, and continuous monitoring.

This special issue focuses on deep learning to address the following topics:

• Preprocessing or fusion of time series data

• Mapping, monitoring, and characterization of land change with time series

• Robust surface parameters retrieval

• Hybrid deep learning and physical models

• Physical interpretation of deep learning models

• Operational products or global datasets

1. Guest editors：

Prof. Hankui Zhang

Geospatial Sciences Center of Excellence, South Dakota State University, USA

[e-mail] 

Prof. Gustau Camps-Valls 

Image Processing Laboratory (IPL), Universitat de València , Spain

[e-mail] 

Prof. Shunlin Liang

Geography Department, University of Hong Kong, China

[e-mail]

Prof. Devis Tuia

Environmental Computational Science and Earth Observation Laboratory, EPFL, Switzerland

[e-mail] 

Prof. Charlotte Pelletier

IRISA, University Bretagne Sud, France

[e-mail] 

Prof. Zhe Zhu

Natural Resources and the Environment, University of Connecticut, USA

[e-mail] 

2. Manuscript submission information：

When submitting your manuscript please select the article type “VSI: Deep learning for TS”. Please submit your manuscript before the submission deadline (31-Oct-2023).

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.

Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage (https://www.elsevier.com/journals/remote-sensing-of-environment/0034-4257/guide-for-authors). 

3. Keywords：

Physics, deep learning, remote sensing, time series, interpretation

Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues

Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors

初审：杨瑞芳复审：宋启凡
终审：金   君

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