Best Paper Award

4.1
Impact Factor
8.6
CiteScore
25 days
Time to First Decision

Remote Sensing Best Paper Award is granted annually to highlight publications of high quality, scientific significance, and extensive influence. The evaluation committee members choose 5 articles of exceptional quality that were published in the journal the year before the previous year and announce them online by the end of June.

The Prizes:
– The first prize (one paper): CHF 1000;
– The second prize (two papers): CHF 800;
– The third prize (two papers): CHF 500.
– Each winner will receive a certificate, and a voucher to waive the Article Processing Charges (APCs) for one submission (review or research article) in Remote Sensing (subject to peer review)— valid for one year.

Remote Sensing Best Paper Award

Winner announcement date: 30 June 2026

Eligibility and Requirements

– All papers published in Remote Sensing will be eligible (both regular and Special Issue submissions).

Selection Criteria

– Scientific merit and broad impact;
– Originality of the research objectives and/or the ideas presented;
– Creativity of the study design or uniqueness of the approaches and concepts;
– Clarity of presentation;
– Citations and downloads.

Past Winners

Winner

Article
45 Citations
10,215 Views
27 Pages
Download PDF
Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product
Christina Eisfelder,
Sarah Asam,
Andreas Hirner,
Philipp Reiners,
Stefanie Holzwarth,
Martin Bachmann,
Ursula Gessner,
Andreas Dietz,
Juliane Huth and
Claudia Kuenzer
+ 1 author
Remote Sens.2023, 15(14), 3616;https://doi.org/10.3390/rs15143616
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20 July 2023
Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation...
Full Article
Article
37 Citations
9,719 Views
27 Pages
Download PDF
Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning
Leonardo F. Arias-Rodriguez,
Ulaş Firat Tüzün,
Zheng Duan,
Jingshui Huang,
Ye Tuo and
Markus Disse
Remote Sens.2023, 15(5), 1390;https://doi.org/10.3390/rs15051390
-
1 March 2023
Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC)....
Full Article
Article
36 Citations
8,284 Views
26 Pages
Download PDF
Continuously Updated Digital Elevation Models (CUDEMs) to Support Coastal Inundation Modeling
Christopher J. Amante,
Matthew Love,
Kelly Carignan,
Michael G. Sutherland,
Michael MacFerrin and
Elliot Lim
Remote Sens.2023, 15(6), 1702;https://doi.org/10.3390/rs15061702
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22 March 2023
The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determinin...
Full Article
Article
78 Citations
5,889 Views
21 Pages
Download PDF
G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
Liping Hou,
Ke Lu,
Xue Yang,
Yuqiu Li and
Jian Xue
Remote Sens.2023, 15(3), 757;https://doi.org/10.3390/rs15030757
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28 January 2023
Typical representations for arbitrary-oriented object detection tasks include the oriented bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet). Each representation encounters problems that correspond to its characte...
Full Article
Article
12 Citations
5,116 Views
38 Pages
Download PDF
Surface ALbedo VALidation (SALVAL) Platform: Towards CEOS LPV Validation Stage 4—Application to Three Global Albedo Climate Data Records
Jorge Sánchez-Zapero,
Enrique Martínez-Sánchez,
Fernando Camacho,
Zhuosen Wang,
Dominique Carrer,
Crystal Schaaf,
Francisco Javier García-Haro,
Jaime Nickeson and
Michael Cosh
Remote Sens.2023, 15(4), 1081;https://doi.org/10.3390/rs15041081
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16 February 2023
The Surface ALbedo VALidation (SALVAL) online platform is designed to allow producers of satellite-based albedo products to move to operational validation systems. The SALVAL tool integrates long-term satellite products, global in situ datasets, and...
Full Article