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Editorial

Announcement: Remote Sensing 2017 Best Reviewer Award Winners

by
Remote Sensing Office
Multidisciplinary Digital Publishing Institute, Alban-Anlage 66, 4052 Basel, Switzerland
Remote Sens. 2018, 10(2), 251; https://doi.org/10.3390/rs10020251
Submission received: 1 February 2018 / Revised: 1 February 2018 / Accepted: 3 February 2018 / Published: 7 February 2018
Peer review is an essential part of the publication process, ensuring that Remote Sensing maintains the high standard of its published papers. Thanks to our reviewers, the median time from submission to first decision has been reduced to 28 days.
The following referees have been selected by the Editor-in-Chief of Remote Sensing, Prof. Prasad Thenkabail, to receive the “Remote Sensing 2017 Best Reviewer Award” based on the quantity, timeliness and quality of their reviews in 2017. The top three will receive 500 CHF, publication of a Feature Paper in Remote Sensing free of charge and a certificate. The other referees will be awarded publication of a Feature Paper in Remote Sensing free of charge and a certificate.
Remotesensing 10 00251 i001 Remotesensing 10 00251 i002
Dr. John C. TrinderDr. Antonio Pepe
The University of New South Wales, AustraliaConsiglio Nazionale delle Ricerche, Italy
Remotesensing 10 00251 i003 Remotesensing 10 00251 i004
Dr. Brian JohnsonDr. Veraldo Liesenberg
Institute for Global Environmental Strategies, JapanSanta Catarina State University, Brazil
Remotesensing 10 00251 i005 Remotesensing 10 00251 i006
Dr. Andrea BuonoDr. Jinyang Du
Università degli Studi di Napoli “Parthenope”, ItalyUniversity of Montana, USA
Remotesensing 10 00251 i007 Remotesensing 10 00251 i008
Dr. Antonio NovelliDr. Saeid Gharechelou
Politecnico di Bari, ItalyChiba University, Japan
Remotesensing 10 00251 i009
Dr. Loris NanniDr. Liming He
University of Padua, ItalyUniversity of Toronto, Canada
On behalf of the Remote Sensing editorial board members and editorial staff, we wish to congratulate the ten outstanding reviewers for their accomplishments.

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MDPI and ACS Style

Remote Sensing Office. Announcement: Remote Sensing 2017 Best Reviewer Award Winners. Remote Sens. 2018, 10, 251. https://doi.org/10.3390/rs10020251

AMA Style

Remote Sensing Office. Announcement: Remote Sensing 2017 Best Reviewer Award Winners. Remote Sensing. 2018; 10(2):251. https://doi.org/10.3390/rs10020251

Chicago/Turabian Style

Remote Sensing Office. 2018. "Announcement: Remote Sensing 2017 Best Reviewer Award Winners" Remote Sensing 10, no. 2: 251. https://doi.org/10.3390/rs10020251

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