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16 February 2023
Increasing Visibility for Preprints.org – Clarivate adds the Preprint Citation Index to the Web of Science

On 9 February 2023, Clarivate, a global leader in providing trusted insights and analytics, added the Preprint Citation Index to the Web of Science platform, streamlining the research process by allowing researchers to locate and link to preprints alongside other trusted content in the database.

The Preprint Citation Index will act as a bridge to connect cutting-edge preprints with peer-reviewed journal articles published within the Web of Science Core Collection. Alerts can be easily set to monitor new research across several repositories and authors will also be able to include preprints on their Web of Science Research Profile to more accurately display their various research outputs.

As of its launch, the Preprint Citation Index will provide nearly two million preprints from various repositories, including MDPI’s own Preprints.org.

MDPI's Preprints Platform – Preprints.org

To advance Open Science and the fast dissemination of research, MDPI offers researchers a free multidisciplinary preprint platform. Preprints.org accepts submissions from all research areas and offers authors high visibility, permanent archiving, article-level Metrics and immediately citable content by assigning a Digital Object Identifier (DOI) to all preprints.

During submission to any MDPI journal, authors have the option to share their research as a preprint. After an initial screening, the manuscript is available online in 48 hours or less. Once online, preprints can be downloaded, shared, commented on, and cited, providing authors maximum visibility.

We invite you to join the ranks of the over 100k researchers using Preprints.org and share your research.

For more information, please visit Preprints.org.

22 December 2022
Special Issue Mentor Program

We are pleased to announce the launch of a new initiative—the MDPI Special Issue Mentor Program.

This program will enable early career researchers (who must hold a Ph.D. in a related field) to experience editing a Special Issue in MDPI journals, under the mentorship of our experienced Editorial Board Members or other experienced scientists. The mentor program will provide an excellent opportunity for early career scientists to gain editorial experience, and to cultivate their ability to edit scientific research.

The mentee’s responsibilities include:

Proposing a Special Issue title and assisting the mentor in preparing a summary (around 200–400 words) and 3–10 keywords describing the background, importance, and goal of the Issue;
Writing a brief promotion plan for the Special Issue;
Preparing a list of scholars who may be interested in the Issue and personally e-mailing invitations on behalf of Guest Editors;
Writing an editorial for the online Special Issue together with the mentor.

The mentor’s responsibilities include:

Conducting a final check before the Special Issue is published online;
Performing editorial control of the Special Issue and quality control of the publications, both of which must be carried out in a timely manner;
Providing suggestions to younger scholars if they have any doubts or concerns regarding submissions;
Organizing video calls with young scholars and the Editorial Office regularly to discuss problems and improvement suggestions for the Special Issue;
Making and submitting decisions regarding submissions with the assistance of mentees.

Certificates and awards:
After the Special Issue closes, the Editorial Office will provide official certificates for all the mentors and early career researchers.

If you are interested in this opportunity, please send your Special Issue proposal to the Editorial Office of a journal you choose, and we will discuss the process (i.e., mentor collaboration, Special Issue topic feasibility analysis, etc.) in further detail. The full list of MDPI journals is as follows: https://www.mdpi.com/about/journals.

In addition to the new Special Issue Mentor Program, we will continue to welcome all Special Issue proposals focusing on hot research topics.

14 December 2022
"Thanks a Million!" – One Million Articles Published in MDPI Journals

MDPI has just become the first open access (OA) publisher to reach the milestone of one million articles published. That is one million articles freely available to all, to circulate and build upon! We are proud to share this special moment with the global scientific community.

This landmark has been reached thanks to the immeasurable support of more than 600,000 expert reviewers, 66,000 editorial board members and 6700 hard-working colleagues across MDPI’s global offices.

Within more than 25 years of publishing, our journals received 2.1 million manuscripts and generated 4.6 million peer review reports to get to one million papers published.

1 Million Infographic

Reaching the milestone of one million articles published reinforces our mission to remove any existing barriers and to make scientific research accessible to all. Since its inception, MDPI’s goal has been to create reliable processes to make science open. This is a path towards facilitating the dissemination of novel insights in scientific communities.

Regular feedback from authors and reviewers shows that our service is greatly appreciated and needed. At the same time, the feedback helps us identify areas for further improvement.

As it stands, a significant share of published research findings remain closed access. More than half of the content published with the most well-known legacy publishers stays behind a paywall, and that is not including articles published in hybrid OA journals, or made available months or years after publication.

A new policy announced by the US administration in August 2022 requires that, as of January 2026, all US federally funded research be made freely and immediately available after publication. While the new policy does not mandate articles be published under an open access license, it is aligned with the open access movement in removing all barriers to research. Similarly, some of the most advanced research institutions in the world intend to have all funded research articles published in open access by 2025.

MDPI is proud to be the leading agent of the transition to open access.

"Thanks a Million" to all the contributors!

8 December 2022
MDPI Sustainability Foundation: New Look and Nominations for the 2023 Sustainability Awards Now Open

We are pleased to announce that the website of the MDPI Sustainability Foundation has been revamped! For the past couple of months, our UX UI team and front-end developers have been working hard to launch the website in time for the opening of the Sustainability Awards nominations.

The website is not the only thing that has had a remodeling. Indeed, the format of the Emerging Sustainability Leader Award (ESLA) has been updated. ESLA is now a competition open to individual researchers or start-ups founded by researchers under the age of 35. Nominee applications will go through 2 rounds of selection until the final 3 are decided. The finalists will then be invited to give pitch presentations during the Award Ceremony to win either first place (10,000 USD) or runner-up (2 x 5000 USD).

The World Sustainability Award, on the other hand, remains the same: a total prize money of 100,000 USD is up for grabs by senior individual researchers or groups of researchers from the international research community.

Nominations for both the World Sustainability Award and the Emerging Sustainability Leader award are now open! Check out our new website for more information on how to nominate.

2 December 2022
**Editorial Board Members from Remote Sensing Featured among the World’s Top 2% Scientists in 2022**

The list of the World’s Top 2% Scientists in 2022 was released by scientists at Stanford on 10 October 2022 to recognize influential scholars around the world. According to the statistics, 220 Remote Sensing Editorial Board Members from different research fields have been selected for the list, which recognizes them for their high-quality research results and outstanding contributions in their fields of expertise.

For the details of the listed scholars, please see the full list below:

Name	Affiliation
Prof. Dr. Adrian Stern	Ben-Gurion University, Israel
Dr. Akram Al-Hourani	RMIT University, Australia
Dr. Alemu Gonsamo	McMaster University, Canada
Dr. Alessandro Matese	1. Institute of BioEconomy, National Research Council (CNR-IBE), Italy; 2. Geosystems Research Institute, Mississippi State University, USA
Prof. Dr. Alexander Brenning	Friedrich Schiller University Jena, Germany
Dr. Alexander Kokhanovsky	German Research Centre for Geosciences, Germany
Prof. Dr. Alfredo Huete	University of Technology Sydney, Australia
Dr. Amin Beiranvand Pour	Universiti Malaysia Terengganu (UMT), Malaysia
Dr. Ana I. de Castro	Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria, Spain
Prof. Dr. Anatoly Gitelson	1. Israel Institute of Technology, Israel; 2. University of Nebraska - Lincoln, USA
Prof. Dr. Andrea Garzelli	University of Siena, Italy
Prof. Dr. Andreas Reigber	German Aerospace Center (DLR), Germany
Prof. Dr. Andrew Skidmore	University of Twente, the Netherlands
Prof. Dr. Andrzej Stateczny	Gdansk Technical University, Poland
Dr. Anup Basu	University of Alberta, Canada
Dr. Arko Lucieer	University of Tasmania, Australia
Prof. Dr. Arturo Sanchez-Azofeifa	University of Alberta, Canada
Dr. Ashraf Dewan	Curtin University, Australia
Prof. Dr. Assefa M. Melesse	Florida International University, USA
Prof. Dr. Atul Jain	University of Illinois, USA
Dr. Augusto Getirana	1. NASA Goddard Space Flight Center, USA; 2. Science Applications International Corporation, USA
Dr. Bailang Yu	East China Normal University, China
Prof. Dr. Long Xiao	China University of Geosciences, China
Prof. Dr. Bas van Wesemael	Université Catholique de Louvain, Belgium
Prof. Dr. Bisheng Yang	Wuhan University, China
Dr. Bo Du	Wuhan University, China
Dr. Brian Alan Johnson	Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Japan
Dr. Bruno Aiazzi	Institute of Applied Physics "Nello Carrara", National Research Council of Italy, Italy
Prof. Dr. Bruno Basso	Michigan State University, USA
Dr. Carlos Alberto Silva	University of Florida, USA
Prof. Dr. Carmine Serio	University of Basilicata, Italy
Dr. Chandra Giri	United States Environmental Protection Agency, USA
Prof. Dr. Changshan Wu	University of Wisconsin-Milwaukee, USA
Prof. Dr. Chaowei Yang	George Mason University, USA
Dr. Chengbin Deng	State University of New York at Binghamton, USA
Dr. Chris Roelfsema	The University of Queensland, Australia
Prof. Dr. Christian Wöhler	TU Dortmund University, Germany
Dr. Christopher D. Elvidge	NOAA National Geophysical Data Center, USA
Prof. Dr. Christopher Small	Columbia University, USA
Dr. Claudia Kuenzer	German Aerospace Center, DLR, Germany
Dr. Claudio Persello	University of Twente, the Netherlands
Dr. Clement Albergel	ESA - European Space Agency, UK
Dr. Clement Atzberger	University of Natural Resources and Life Sciences, Austria
Prof. Dr. Conghe Song	University of North Carolina at Chapel Hill, USA
Prof. Dr. Costas Varotsos	National and Kapodistrian University of Athens, Greece
Prof. Dr. Danfeng Hong	The Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, China
Prof. Dr. Danilo Orlando	Università degli Studi “Niccolò Cusano”, Italy
Prof. Dr. Danlin Yu	Montclair State University, USA
Dr. Dar Roberts	University of California, USA
Dr. David M. Johnson	USDA / National Agricultural Statistics Service, USA
Prof. Dr. David Skole	Michigan State University, USA
Dr. David W. Johnston	Duke University, USA
Prof. Dr. Debra F. Laefer	1. New York University, USA; 2. University College Dublin, Ireland
Prof. Dr. Dehua Mao	Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, China
Prof. Dr. Dengsheng Lu	Fujian Normal University, China
Dr. Dino Ienco	French National Institute for Agriculture, Food and Environment, Department of Territories, France
Dr. Dominik Seidel	University of Göttingen, Germany
Dr. Dominique Arrouays	INRAE, InfoSol Unit, France
Dr. Edoardo Pasolli	University of Naples Federico II, Italy
Dr. Eileen H. Helmer	United States Department of Agriculture, USA
Prof. Dr. Eric Small	University of Colorado, USA
Dr. Eric Vermote	NASA Goddard Space Flight Center, USA
Dr. Eugenio Sansosti	Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), National Research Council (CNR) of Italy, Italy
Dr. Eyal Ben-Dor	Tel Aviv University (TAU), Israel
Prof. Dr. Filippo Catani	University of Florence, Italy
Prof. Dr. Francesco Martellotta	Politecnico di Bari, Italy
Dr. Francesco Mattia	National Research Council of Italy (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), Italy
Dr. Francesco Nex	University of Twente, the Netherlands
Dr. Francesco Soldovieri	National Research Council of Italy (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), Italy
Dr. Frédéric Frappart	INRAE, Université de Bordeaux, France
Prof. Dr. Fumio Yamazaki	1. Chiba University, Japan; 2. National Research Institute for Earth Science and Disaster Resilience (NIED), Japan
Dr. Gabriel B. Senay	USGS EROS Center, North Central Climate Adaptation Science Center, USA
Dr. Gemine Vivone	Institute of Methodologies for Environmental Analysis, CNR-IMAA, Italy
Dr. Geoffrey Parker	Smithsonian Environmental Research Center, USA
Dr. George P. Petropoulos	Harokopio University of Athens, Greece
Dr. George Xian	USGS Center for Earth Resources Observation and Science, USA
Dr. Gianpaolo Balsamo	European Centre for Medium-range Weather Forecasts, UK
Dr. Gianpaolo Coro	National Research Council of Italy (CNR), Institute of Information Science and Technologies "Alessandro Faedo" (ISTI-CNR), Italy
Prof. Dr. Giles Foody	University of Nottingham, UK
Prof. Dr. Giuseppe Modica	Università degli Studi Mediterranea di Reggio Calabria, Italy
Prof. Dr. Greg Okin	University of California, USA
Dr. Guillaume Ramillien	Géosciences Environnement Toulouse, CNRS/IRD/UPS, Observatoire Midi-Pyrénées, France
Dr. Guoqing Zhou	1. Guilin Iniversity of Technology, China; 2. Tianjin University, China
Dr. Gwanggil Jeon	Incheon National University, South Korea
Dr. Hamish D. Pritchard	British Antarctic Survey, UK
Prof. Dr. Hongtao Duan	Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, China
Prof. Dr. Hubert Hasenauer	BOKU University of Natural Resources and Life Sciences, Austria
Prof. Dr. Ignacio A. Ciampitti	Kansas State University, USA
Prof. Dr. Inge Sandholt	Technical University of Denmark, Denmark
Dr. Ira Leifer	Bubbleology Research International, USA
Prof. Dr. Isabel Trigo	(IPMA) EUMETSAT Land Surface Analysis - Satellite Application, Facility Project Manager Rua C ao Aeroporto, Portugal
Prof. Dr. Ismail Gultepe	Ontario Technical University, Canada
Prof. Dr. James Campbell	Virginia Polytechnic Institute and State University (Virginia Tech), USA
Prof. Dr. James Carton	University of Maryland, USA
Prof. Dr. Janet E. Nichol	University of Sussex, UK
Dr. Janne Heiskanen	University of Helsinki, Finland
Prof. Dr. Jan-Peter Muller	UCL Mullard Space Science Laboratory, UK
Dr. Jean-Christophe Calvet	CNRM, Meteo-France, France
Dr. Jean-Louis Roujean	CESBIO, Toulouse, France
Prof. Dr. Jeffrey F. Kelly	Plains Institute, University of Oklahoma, USA
Prof. Dr. Jianghui Geng	Wuhan University, China
Prof. Dr. Jianxi Huang	China Agricultural University, China
Prof. Dr. Jie Shan	Purdue University, USA
Dr. Jin Wu	The University of Hong Kong, Hong Kong, China
Dr. Jochem Verrelst	University of Valencia, Spain
Prof. Dr. Jörg Bendix	Laboratory for Climatology and Remote Sensing, Phillips- University of Marburg, Germany
Prof. Dr. Jose Moreno	Universitat de València, Spain
Prof. Dr. Josep Peñuelas	Global Ecology Unit CREAF‐CSIC‐UAB, Spain
Prof. Dr. Juha Hyyppä	Finish Geospatial Research Institute, Finland
Prof. Dr. Jungho Im	Ulsan National Institute of Science and Technology, South Korea
Prof. Dr. Junjun Jiang	Harbin Institute of Technology, China
Dr. Junshi Xia	Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Japan
Dr. Justin Morgenroth	University of Canterbury, New Zealand
Prof. Dr. Kaicun Wang	Peking University, China
Dr. Kim Calders	Ghent University, Belgium
Dr. Kohei Arai	Saga University, Japan
Dr. Konstantinos Topouzelis	University of the Aegean, Greece
Dr. Konstantinos X. Soulis	Agricultural University of Athens, Greece
Dr. Krzysztof Stereńczak	Forest Research Institute, Poland
Prof. Dr. Lalit Kumar	East Coast Geospatial Consultants, Australia
Prof. Dr. Lefei Zhang	Wuhan University, China
Dr. Leonor Calvo	Universidad de León, Spain
Prof. Dr. Liping Di	George Mason University, USA
Prof. Dr. Lizhe Wang	Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China
Dr. Luca Brocca	National Research Council, Research Institute for Geo-Hydrological Protection, Italy
Prof. Dr. Lunche Wang	China University of Geosciences, China
Prof. Dr. Marco Scaioni	Politecnico di Milano, Italy
Dr. Martin Mlynczak	NASA Langley Research Center, USA
Prof. Dr. Massimiliano Pieraccini	University of Florence, Italy
Prof. Dr. Massimo Menenti	Aerospace Information Research Institute, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, China
Dr. Matthew Clarke	Sonoma State University, USA
Prof. Dr. Matthew McCabe	King Abdullah University of Science and Technology, Saudi Arabia
Dr. Mauro Dalla Mura	GIPSA-Lab, Grenoble Institute of Technology, France
Dr. Mehrez Zribi	Universite Paul Sabatier Toulouse III, Italy
Prof. Dr. Miaogen Shen	Beijing Normal University, China
Dr. Michael H. F. Wilkinson	University of Groningen, the Netherlands
Prof. Dr. Michael Lefsky	Colorado State University, USA
Prof. Dr. Michael Vohland	Leipzig University, Germany
Dr. Michele Meroni	European Commission, Joint Research Centre, Directorate D – Sustainable Resources, Food Security Unit, Italy
Dr. Mohammad Awrangjeb	Griffith University, Australia
Dr. Nancy E. Grulke	USDA Forest Service Pacific Northwest Research Station, USA
Dr. Nicolas Baghdadi	University of Montpellier, France
Prof. Dr. Noam Levin	The Hebrew University of Jerusalem, Israel
Dr. Okan Yurduseven	Queen’s University Belfast, UK
Dr. Oleg Dubovik	Laboratoire d’Optique Atmosphérique, CNRS/Universite Lille, France
Dr. Olivier Merlin	CESBIO, Université de Toulouse, France
Dr. Pamela Nagler	U.S. Geological Survey, Southwest Biological Science Center, USA
Dr. Parth Sarathi Roy	Sustainable Landscapes and Restoration, World Resources Institute India, India
Dr. Paul Honeine	LITIS Lab, Université de Rouen Normandie, France
Prof. Dr. Paul Scheunders	Vision Lab, University of Antwerp (CDE), Belgium
Dr. Paulo Pereira	Environmental Management Laboratory, Mykolas Romeris University, Lithuania
Prof. Dr. L. Monika Moskal	University of Washington (UW), USA
Dr. Peng Fu	Harrisburg University, USA
Prof. Dr. Peng Jia	Wuhan University, China
Prof. Dr. Pinliang Dong	University of North Texas, USA
Prof. Dr. Piotr Samczynski	Warsaw University of Technology, Poland
Prof. Dr. Prem Prakash Jayaraman	Swinburne University of Technology, Australia
Prof. Dr. Qi Wang	Northwestern Polytechnical University, China
Dr. Qiangqiang Yuan	Wuhan University, China
Prof. Dr. Qihao Weng	Indiana State University, USA
Prof. Dr. Qile Zhao	Wuhan University, China
Dr. Qinghua Guo	State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, China
Prof. Dr. Qiuhong Tang	Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China
Dr. Qiusheng Wu	University of Tennessee, USA
Dr. Ralph R. Ferraro	NOAA/NESDIS/Center for Satellite Applications and Research (STAR), USA
Prof. Dr. Raphael M. Kudela	University of California, USA
Dr. Robert Brewin	University of Exeter (Penryn Campus), UK
Dr. Robert Treuhaft	Jet Propulsion Laboratory, California Institute of Technology, USA
Dr. Ronald C. Estoque	Forestry and Forest Products Research Institute (FFPRI), Japan
Dr. Ronan Fablet	Institut Mines-Télécom, Telecom-Bretagne, France
Dr. Rosa Lasaponara	CNR-IMAA (Institute of Environmental Analysis), Italy
Prof. Dr. Ruiliang Pu	University of South Florida, USA
Prof. Dr. Salah Bourennane	Ecole Centrale de Marseille, France
Dr. Sandra Eckert	University of Bern, Switzerland
Prof. Dr. Sébastien Lefèvre	IRISA, Université Bretagne Sud, Campus de Tohannic, France
Dr. Seyed Amir Naghibi	Lund University, Sweden
Dr. Shawn P. Serbin	Brookhaven National Laboratory, USA
Prof. Dr. Shuanggen Jin	Shanghai Astronomical Observatory, Chinese Academy of Sciences, China
Dr. Shubha Sathyendranath	Plymouth Marine Laboratory, UK
Prof. Dr. Shuguang Liu	Central South University of Forestry and Technology, China
Prof. Dr. Shunichi Koshimura	Tohoku University, Japan
Prof. Dr. Simon Jones	RMIT University, Australia
Prof. Dr. Soe W. Myint	Arizona State University, USA
Dr. Soo Chin Liew	National University of Singapore, Singapore
Prof. Dr. Stefania Bonafoni	University of Perugia, Italy
Dr. Stefano Mattoccia	University of Bologna, Italy
Dr. Stefano Tebaldini	Politecnico di Milano, Department of Information, Electronics, and Bioengineering, Italy
Prof. Dr. Stuart Phinn	University of Queensland, Australia
Prof. Dr. Tamas Sziranyi	Machine Perception Research Laboratory, Hungary
Dr. Tao Lei	Shaanxi University of Science and Technology, China
Prof. Dr. Thomas Udelhoven	University of Trier, Germany
Prof. Dr. Toby N. Carlson	Pennsylvania State University, USA
Dr. Tomoaki Miura	University of Hawai‘i at Mānoa, USA
Prof. Dr. Valerio Tramutoli	University of Basilicata, Italy
Dr. W. Gareth Rees	University of Cambridge, UK
Prof. Dr. Weiqi Zhou	State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, China
Prof. Dr. Wenge Ni-meister	Hunter College The City University of New York, USA
Prof. Dr. Wenhui Kuang	Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
Dr. Wenjiang Huang	Aerospace Information Research Institute, Chinese Academy of Sciences, China
Dr. Xia Yao	Nanjing Agricultural University, China
Dr. Xian Sun	Aerospace Information Research Institute, Chinese Academy of Sciences, China
Prof. Dr. Xiangrong Zhang	Xidian University, China
Prof. Dr. Xiaohua Tong	Tongji University, China
Dr. Xiaoxiong Xiong	Sciences and Exploration Directorate, NASA Goddard Space Flight Center, USA
Dr. Xin Li	Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, China
Dr. Xuecao Li	China Agricultural University, China
Prof. Dr. Yanfei Zhong	Wuhan University, China
Prof. Dr. Yang Hong	University of Oklahoma, USA
Prof. Dr. Yongjiu Dai	Sun Yat-sen University, China
Dr. Yongxiang Hu	NASA Langley Research Center, USA
Dr. Yoshio Inoue	University of Tokyo, Japan
Dr. Yuanwei Qin	University of Oklahoma, USA
Prof. Dr. Yuji Murayama	University of Tsukuba, Japan
Prof. Dr. Yunlin Zhang	Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, China
Dr. Yuwei Chen	Finnish Geospatial Research Institute, Finland
Dr. Yuxin Miao	University of Minnesota, USA
Dr. Zhe Zhu	University of Connecticut, USA
Prof. Dr. Zhenhong Li	Chang’an University, China
Prof. Dr. Zhenwei Shi	Beihang University, China
Prof. Dr. Zhong Lu	Southern Methodist University, USA

Congratulations to the scholars again!

Source: September 2022 data update for “Updated science-wide author databases of standardized citation indicators”: https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/4.

2 December 2022
**Editorial Board Members from Remote Sensing Featured in the 2022 Highly Cited Researchers List Published by Clarivate**

Recently, Clarivate™ revealed its 2022 list of Highly Cited Researchers™—individuals at universities, research institutes and commercial organizations.

The scientists who were selected into this year’s list of Highly Cited Researchers have published highly cited papers in the 11-year period from January 2011 to December 2021, with citation frequency in the top 1% of academic subjects and the same year of publication in the Web of Science™ database. Based on Web of Science Citation data, 6,938 researchers from across the globe who have demonstrated a disproportionate level of significant and broad influence in their chosen field or fields over the last decade have been awarded Highly Cited Researcher 2022 designations. The list is truly global, spanning 69 countries or regions and spread across a diverse range of research sciences and social sciences.

According to statistics, 27 members of the Editorial Board of Remote Sensing (ISSN: 2072-4292) have been selected into the list of Highly Cited Researchers of Clarivate in 2022. They are being recognized for their high-quality scientific research achievements and outstanding contributions to professional fields. The Remote Sensing journal office sincerely congratulates all elected Editorial Board Members and hopes that they continue to have an academically productive relationship with the journal.

Name	Affiliation
Dr. Arko Lucieer	University of Tasmania, Australia
Prof. Dr. Atul Jain	University of Illinois Urbana-Champaign, USA
Prof. Dr. Bas van Wesemael	Universite Catholique Louvain, Belgium
Dr. Bo Du	Wuhan University, China
Prof. Dr. Danfeng Hong	Chinese Academy of Sciences, China
Prof. Dr. Eric Rignot	University of California Irvine, USA
Dr. Gianpaolo Balsamo	European Centre for Medium-Range Weather Forecasts (ECMWF), UK
Prof. Dr. Jon Atli Benediktsson	University of Iceland, Iceland
Prof. Dr. Junjun Jiang	Harbin Institute of Technology, China
Prof. Dr. Lefei Zhang	Wuhan University, China
Prof. Dr. Liangpei Zhang	Wuhan University, China
Prof. Dr. Licheng Jiao	Xidian University, China
Prof. Dr. Xiaoping Liu	Sun Yat-Sen University, China
Prof. Dr. Matthew F. McCabe	King Abdullah University of Science & Technology, Saudi Arabia
Dr. Naoto Yokoya	University of Tokyo, Japan
Prof. Dr. Nicholas C. Coops	University of British Columbia, Canada
Dr. Pedram Ghamisi	Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany
Prof. Dr. Qi Wang	Northwestern Polytechnical University, China
Prof. Dr. Qian Du	Mississippi State University, USA
Dr. Qiangqiang Yuan	Wuhan University, China
Dr. Shutao Li	Hunan University, China
Prof. Dr. Weiqi Zhou	Chinese Academy of Sciences, China
Prof. Dr. Yang Hong	Tsinghua University, China
Prof. Dr. Yu Liu	Peking University, China
Dr. Yuanwei Qin	University of Oklahoma System, USA
Dr. Yuyu Zhou	Iowa State University, USA
Dr. Zhe Zhu	University of Connecticut, USA

8 November 2022
Meet Us at the 13th China Satellite Navigation Conference, 16–18 November 2022, Beijing, China

Conference: The 13th China Satellite Navigation Conference
Date: 16–18 November 2022
Place: Beijing, China

Sensors (ISSN: 1424-8220) will be attending the 13th China Satellite Navigation Conference as an exhibitor. This meeting will take place from 16 to 18 November in Beijing, China.

The China Satellite Navigation Conference (CSNC) is an open academic exchange platform. It aims to strengthen academic innovation and promote the cooperation and exchange of satellite navigation systems, strengthen technological innovation and promote the engineering construction of satellite navigation systems, strengthen theoretical innovation and promote the progress of satellite navigation theories, and strengthen application innovation and promote the scientific development of satellite navigation industry. The conference has been successfully held for 12 years and has produced a large number of excellent results in academic, technical, theoretical, application, and talent aspects.

The following MDPI journals will be represented:

Sensors;
Remote Sensing;
Atmosphere;
Aerospace;
Technologies;
Drones;
Signals;
Smart Cities;
AI;
Geomatics.

If you are planning to attend this conference, please do not hesitate to start an online conversation with us (booth #B18). Our delegates look forward to meeting you in person and answering any questions that you may have. For more information about the conference and virtual booth, please visit the following website: https://www.beidou.org/annualmeeting.html.

31 October 2022
Remote Sensing | Editor’s Choice Articles in 2020

We are pleased to invite you to read the Editor’s Choice Articles in Remote Sensing (ISSN: 2072-4292). The list of high-quality and interesting papers that were specifically recommended by our Editorial Board Members can be found at the following link: https://www.mdpi.com/journal/remotesensing/editors_choice. The paper list is as follows:

1. “Feasibility of Burned Area Mapping Based on ICESAT−2 Photon Counting Data” by Liu, M. et al. Remote Sens. 2020, 12(1), 24; https://doi.org/10.3390/rs12010024 Available online: https://www.mdpi.com/2072-4292/12/1/24	2. “Potential of Night-Time Lights to Measure Regional Inequality” by Ivan, K. et al. Remote Sens. 2020, 12(1), 33; https://doi.org/10.3390/rs12010033 Available online: https://www.mdpi.com/2072-4292/12/1/33
3. “Antarctic Supraglacial Lake Detection Using Landsat 8 and Sentinel-2 Imagery: Towards Continental Generation of Lake Volumes” by Moussavi, M. et al. Remote Sens. 2020, 12(1), 134; https://doi.org/10.3390/rs12010134 Available online: https://www.mdpi.com/2072-4292/12/1/134	4. “Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks” by Gao, J. et al. Remote Sens. 2020, 12(1), 191; https://doi.org/10.3390/rs12010191 Available online: https://www.mdpi.com/2072-4292/12/1/191
5. “Evaluation of Coherent and Incoherent Landslide Detection Methods Based on Synthetic Aperture Radar for Rapid Response: A Case Study for the 2018 Hokkaido Landslides” by Jung, J. et al. Remote Sens. 2020, 12(2), 265; https://doi.org/10.3390/rs12020265 Available online: https://www.mdpi.com/2072-4292/12/2/265	6. “Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)” by Nguyen, M. et al. Remote Sens. 2020, 12(2), 281; https://doi.org/10.3390/rs12020281 Availablle online: https://www.mdpi.com/2072-4292/12/2/281
7. “Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion” by Heckel, K. et al. Remote Sens. 2020, 12(2), 302; https://doi.org/10.3390/rs12020302 Available online: https://www.mdpi.com/2072-4292/12/2/302	8. “Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services” by Barbierato, E. et al. Remote Sens. 2020, 12(2), 329; https://doi.org/10.3390/rs12020329 Available online: https://www.mdpi.com/2072-4292/12/2/329
9. “Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models” by Prakash, N. et al. Remote Sens. 2020, 12(3), 346; https://doi.org/10.3390/rs12030346 Available online: https://www.mdpi.com/2072-4292/12/3/346	10. “Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas” by Lapini, A. et al. Remote Sens. 2020, 12(3), 369; https://doi.org/10.3390/rs12030369 Available online: https://www.mdpi.com/2072-4292/12/3/369
11. “How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?” by Zhang, X. et al. Remote Sens. 2020, 12(3), 417; https://doi.org/10.3390/rs12030417 Available online: https://www.mdpi.com/2072-4292/12/3/417	12. “LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor” by Morishita, Y. et al. Remote Sens. 2020, 12(3), 424; https://doi.org/10.3390/rs12030424 Available online: https://www.mdpi.com/2072-4292/12/3/424
13. “Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A/B Satellites Mission” by Caballero, I. et al. Remote Sens. 2020, 12(3), 451; https://doi.org/10.3390/rs12030451 Available online: https://www.mdpi.com/2072-4292/12/3/451	14. “Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis” by Saha, K. et al. Remote Sens. 2020, 12(4), 590; https://doi.org/10.3390/rs12040590 Available online: https://www.mdpi.com/2072-4292/12/4/590
15. “Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine” by Li, Q. et al. Remote Sens. 2020, 12(4), 602; https://doi.org/10.3390/rs12040602 Available online: https://www.mdpi.com/2072-4292/12/4/602	16. *“A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery”* by Mora-Soto, A. et al. Remote Sens. 2020, 12(4), 694; https://doi.org/10.3390/rs12040694 Available online: https://www.mdpi.com/2072-4292/12/4/694
17. “The Spatial and Spectral Resolution of ASTER Infrared Image Data: A Paradigm Shift in Volcanological Remote Sensing” by Ramsey, M. et al. Remote Sens. 2020, 12(4), 738; https://doi.org/10.3390/rs12040738 Available online: https://www.mdpi.com/2072-4292/12/4/738	18. “Land-Cover Changes to Surface-Water Buffers in the Midwestern USA: 25 Years of Landsat Data Analyses (1993–2017)” by Berhane, T. et al. Remote Sens. 2020, 12(5), 754; https://doi.org/10.3390/rs12050754 Available online: https://www.mdpi.com/2072-4292/12/5/754
19. “Sentinel-1 DInSAR for Monitoring Active Landslides in Critical Infrastructures: The Case of the Rules Reservoir (Southern Spain)” by Reyes-Carmona, C. et al. Remote Sens. 2020, 12(5), 809; https://doi.org/10.3390/rs12050809 Available online: https://www.mdpi.com/2072-4292/12/5/809	20. “Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions” by Naser, M. et al. Remote Sens. 2020, 12(5), 824; https://doi.org/10.3390/rs12050824 Available online: https://www.mdpi.com/2072-4292/12/5/824
21. “Combining InfraRed Thermography and UAV Digital Photogrammetry for the Protection and Conservation of Rupestrian Cultural Heritage Sites in Georgia: A Methodological Application” by Frodella, W. et al. Remote Sens. 2020, 12(5), 892; https://doi.org/10.3390/rs12050892 Available online: https://www.mdpi.com/2072-4292/12/5/892	22. “Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform” by Alencar, A. et al. Remote Sens. 2020, 12(6), 924; https://doi.org/10.3390/rs12060924 Available online: https://www.mdpi.com/2072-4292/12/6/924
23. “Applications of Unmanned Aerial Vehicles in Cryosphere: Latest Advances and Prospects” by Gaffey, C. et al. Remote Sens. 2020, 12(6), 948; https://doi.org/10.3390/rs12060948 Available online: https://www.mdpi.com/2072-4292/12/6/948	24. “On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data” by Militino, A. et al. Remote Sens. 2020, 12(6), 1008; https://doi.org/10.3390/rs12061008 Available online: https://www.mdpi.com/2072-4292/12/6/1008
25. “Accounting for Training Data Error in Machine Learning Applied to Earth Observations” by Elmes, A. et al. Remote Sens. 2020, 12(6), 1034; https://doi.org/10.3390/rs12061034 Available online: https://www.mdpi.com/2072-4292/12/6/1034	26. “Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks” by Nezami, S. et al. Remote Sens. 2020, 12(7), 1070; https://doi.org/10.3390/rs12071070 Available online: https://www.mdpi.com/2072-4292/12/7/1070
27. “Remote Sensing of River Discharge: A Review and a Framing for the Discipline” by Gleason, C. et al. Remote Sens. 2020, 12(7), 1107; https://doi.org/10.3390/rs12071107 Available online: https://www.mdpi.com/2072-4292/12/7/1107	28. “Regional Dependence of Atmospheric Responses to Oceanic Eddies in the North Pacific Ocean” by Ji, J. et al. Remote Sens. 2020, 12(7), 1161; https://doi.org/10.3390/rs12071161 Available online: https://www.mdpi.com/2072-4292/12/7/1161
29. “Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing” by Fu, D. et al. Remote Sens. 2020, 12(7), 1193; https://doi.org/10.3390/rs12071193 Available online: https://www.mdpi.com/2072-4292/12/7/1193	30. “Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme” by Mäkynen, M. et al. Remote Sens. 2020, 12(7), 1214; https://doi.org/10.3390/rs12071214 Available online: https://www.mdpi.com/2072-4292/12/7/1214
31. “The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales” by Avian, M. et al. Remote Sens. 2020, 12(8), 1251; https://doi.org/10.3390/rs12081251 Available online: https://www.mdpi.com/2072-4292/12/8/1251	32. “An Overview of Platforms for Big Earth Observation Data Management and Analysis” by Gomes, V. et al. Remote Sens. 2020, 12(8), 1253; https://doi.org/10.3390/rs12081253 Available online: https://www.mdpi.com/2072-4292/12/8/1253
33. “Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy” by Falanga Bolognesi, S. et al. Remote Sens. 2020, 12(8), 1275; https://doi.org/10.3390/rs12081275 Available online: https://www.mdpi.com/2072-4292/12/8/1275	34. “Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants” by Sukhova, E. et al. Remote Sens. 2020, 12(8), 1312; https://doi.org/10.3390/rs12081312 Available online: https://www.mdpi.com/2072-4292/12/8/1312
35. “Near Real-Time Monitoring of the Christmas 2018 Etna Eruption Using SEVIRI and Products Validation” by Corradini, S. et al. Remote Sens. 2020, 12(8), 1336; https://doi.org/10.3390/rs12081336 Available online: https://www.mdpi.com/2072-4292/12/8/1336	36. “Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8” by Ma, X. et al. Remote Sens. 2020, 12(8), 1339; https://doi.org/10.3390/rs12081339 Available online: https://www.mdpi.com/2072-4292/12/8/1339
37. “High Quality Zenith Tropospheric Delay Estimation Using a Low-Cost Dual-Frequency Receiver and Relative Antenna Calibration” by Krietemeyer, A. et al. Remote Sens. 2020, 12(9), 1393; https://doi.org/10.3390/rs12091393 Available online: https://www.mdpi.com/2072-4292/12/9/1393	38. “Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity” by LaRue, E. et al. Remote Sens. 2020, 12(9), 1407; https://doi.org/10.3390/rs12091407 Available online: https://www.mdpi.com/2072-4292/12/9/1407
39. “Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification” by Scholl, V. et al. Remote Sens. 2020, 12(9), 1414; https://doi.org/10.3390/rs12091414 Available online: https://www.mdpi.com/2072-4292/12/9/1414	40. “A. Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review” by Abdollahi, A. et al. Remote Sens. 2020, 12(9), 1444; https://doi.org/10.3390/rs12091444 Available online: https://www.mdpi.com/2072-4292/12/9/1444
41. “Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning” by Chaves, P. et al. Remote Sens. 2020, 12(9), 1523; https://doi.org/10.3390/rs12091523 Available online: https://www.mdpi.com/2072-4292/12/9/1523	42. “U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil” by Wagner, F. et al. Remote Sens. 2020, 12(10), 1544; https://doi.org/10.3390/rs12101544 Available online: https://www.mdpi.com/2072-4292/12/10/1544
43. “LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest” by Stefanidou, A. et al. Remote Sens. 2020, 12(10), 1565; https://doi.org/10.3390/rs12101565 Available online: https://www.mdpi.com/2072-4292/12/10/1565	44. “Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement” by Krisanski, S. et al. Remote Sens. 2020, 12(10), 1652; https://doi.org/10.3390/rs12101652 Available online: https://www.mdpi.com/2072-4292/12/10/1652
45. “60 Years of Glacier Elevation and Mass Changes in the Maipo River Basin, Central Andes of Chile” by Farías-Barahona, D. et al. Remote Sens. 2020, 12(10), 1658; https://doi.org/10.3390/rs12101658 Available online: https://www.mdpi.com/2072-4292/12/10/1658	46. “Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends” by Hoeser, T. et al. Remote Sens. 2020, 12(10), 1667; https://doi.org/10.3390/rs12101667 Available online: https://www.mdpi.com/2072-4292/12/10/1667
47. “Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil” by d’Oliveira, M. et al. Remote Sens. 2020, 12(11), 1754; https://doi.org/10.3390/rs12111754 Available online: https://www.mdpi.com/2072-4292/12/11/1754	48. “Adaptive Modeling of the Global Ionosphere Vertical Total Electron Content” by Erdogan, E. et al. Remote Sens. 2020, 12(11), 1822; https://doi.org/10.3390/rs12111822 Available online: https://www.mdpi.com/2072-4292/12/11/1822
49. “Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories” by McRoberts, R. et al. Remote Sens. 2020, 12(11), 1891; https://doi.org/10.3390/rs12111891 Available online: https://www.mdpi.com/2072-4292/12/11/1891	50. “Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data” by Zhang, Z. et al. Remote Sens. 2020, 12(12), 1927; https://doi.org/10.3390/rs12121927 Available online: https://www.mdpi.com/2072-4292/12/12/1927
51. “An Estimation of Top-Down NOx Emissions from OMI Sensor Over East Asia” by Han, K. et al. Remote Sens. 2020, 12(12), 2004; https://doi.org/10.3390/rs12122004 Available online: https://www.mdpi.com/2072-4292/12/12/2004	52. “From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States” by Mucia, A. et al. Remote Sens. 2020, 12(12), 2020; https://doi.org/10.3390/rs12122020 Available online: https://www.mdpi.com/2072-4292/12/12/2020
53. “Surface Temperature of the Planet Earth from Satellite Data over the Period 2003–2019” by Sobrino, J. et al. Remote Sens. 2020, 12(12), 2036; https://doi.org/10.3390/rs12122036 Available online: https://www.mdpi.com/2072-4292/12/12/2036	54. “Analysis and Assessment of BDS-2 and BDS-3 Broadcast Ephemeris: Accuracy, the Datum of Broadcast Clocks and Its Impact on Single Point Positioning” by Jiao, G. et al. Remote Sens. 2020, 12(13), 2081; https://doi.org/10.3390/rs12132081 Available online: https://www.mdpi.com/2072-4292/12/13/2081
55. “Sea Level Variability in the Red Sea: A Persistent East–West Pattern” by Abdulla, C. et al. Remote Sens. 2020, 12(13), 2090; https://doi.org/10.3390/rs12132090 Available online: https://www.mdpi.com/2072-4292/12/13/2090	56. “Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS” by Raksapatcharawong, M. et al. Remote Sens. 2020, 12(13), 2099; https://doi.org/10.3390/rs12132099 Available online: https://www.mdpi.com/2072-4292/12/13/2099
57. “Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform” by Deng, X. et al. Remote Sens. 2020, 12(13), 2153; https://doi.org/10.3390/rs12132153 Available online: https://www.mdpi.com/2072-4292/12/13/2153	58. “EANet: Edge-Aware Network for the Extraction of Buildings from Aerial Images” by Yang, G. et al. Remote Sens. 2020, 12(13), 2161; https://doi.org/10.3390/rs12132161 Available online: https://www.mdpi.com/2072-4292/12/13/2161
59. “Development of the Chinese Space-Based Radiometric Benchmark Mission LIBRA” by Zhang, P. et al. Remote Sens. 2020, 12(14), 2179; https://doi.org/10.3390/rs12142179 Available online: https://www.mdpi.com/2072-4292/12/14/2179	60. “Gas Emission Craters and Mound-Predecessors in the North of West Siberia, Similarities and Differences” by Kizyakov, A. et al. Remote Sens. 2020, 12(14), 2182; https://doi.org/10.3390/rs12142182 Available online: https://www.mdpi.com/2072-4292/12/14/2182
61. “Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements” by Wang, S. et al. Remote Sens. 2020, 12(14), 2204; https://doi.org/10.3390/rs12142204 Available online: https://www.mdpi.com/2072-4292/12/14/2204	62. “Sentinel-2 Data for Land Cover/Use Mapping: A Review” by Phiri, D. et al. Remote Sens. 2020, 12(14), 2291; https://doi.org/10.3390/rs12142291 Available online: https://www.mdpi.com/2072-4292/12/14/2291
63. “Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review” by El Mahrad, B. et al. Remote Sens. 2020, 12(14), 2313; https://doi.org/10.3390/rs12142313 Available online: https://www.mdpi.com/2072-4292/12/14/2313	64. “Estimating River Sediment Discharge in the Upper Mississippi River Using Landsat Imagery” by A. Flores, J. et al. Remote Sens. 2020, 12(15), 2370; https://doi.org/10.3390/rs12152370 Available online: https://www.mdpi.com/2072-4292/12/15/2370
65. “Assessment of Tree Detection Methods in Multispectral Aerial Images” by Pulido, D. et al. Remote Sens. 2020, 12(15), 2379; https://doi.org/10.3390/rs12152379 Available online: https://www.mdpi.com/2072-4292/12/15/2379	66. “Multi-Year Comparison of CO2 Concentration from NOAA Carbon Tracker Reanalysis Model with Data from GOSAT and OCO-2 over Asia” by Mustafa, F. et al. Remote Sens. 2020, 12(15), 2498; https://doi.org/10.3390/rs12152498 Available online: https://www.mdpi.com/2072-4292/12/15/2498
67. “Vegetation Detection Using Deep Learning and Conventional Methods” by Ayhan, B. et al. Remote Sens. 2020, 12(15), 2502; https://doi.org/10.3390/rs12152502 Available online: https://www.mdpi.com/2072-4292/12/15/2502	68. “Classification of Urban Area Using Multispectral Indices for Urban Planning” by Lynch, P. et al. Remote Sens. 2020, 12(15), 2503; https://doi.org/10.3390/rs12152503 Available online: https://www.mdpi.com/2072-4292/12/15/2503
69. “Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature” by Merchant, C. et al. Remote Sens. 2020, 12(16), 2554; https://doi.org/10.3390/rs12162554 Available online: https://www.mdpi.com/2072-4292/12/16/2554	70. “Land Surface Temperature Retrieval from Passive Microwave Satellite Observations: State-of-the-Art and Future Directions” by Duan, S. et al. Remote Sens. 2020, 12(16), 2573; https://doi.org/10.3390/rs12162573 Available online: https://www.mdpi.com/2072-4292/12/16/2573
71. “Variations of Mass Balance of the Greenland Ice Sheet from 2002 to 2019” by Mu, Y. et al. Remote Sens. 2020, 12(16), 2609; https://doi.org/10.3390/rs12162609 Available online: https://www.mdpi.com/2072-4292/12/16/2609	72. “Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths” by Herbert, C. et al. Remote Sens. 2020, 12(16), 2614; https://doi.org/10.3390/rs12162614 Available online: https://www.mdpi.com/2072-4292/12/16/2614
73. “Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies” by Santana, O. et al. Remote Sens. 2020, 12(16), 2625; https://doi.org/10.3390/rs12162625 Available online: https://www.mdpi.com/2072-4292/12/16/2625	74. “The ESA Permanent Facility for Altimetry Calibration: Monitoring Performance of Radar Altimeters for Sentinel-3A, Sentinel-3B and Jason-3 Using Transponder and Sea-Surface Calibrations with FRM Standards” by Mertikas, S. et al. Remote Sens. 2020, 12(16), 2642; https://doi.org/10.3390/rs12162642 Available online: https://www.mdpi.com/2072-4292/12/16/2642
75. “Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture” by Lu, B. et al. Remote Sens. 2020, 12(16), 2659; https://doi.org/10.3390/rs12162659 Available online: https://www.mdpi.com/2072-4292/12/16/2659	76. “Application of Convolutional Neural Network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation” by Le, X. et al. Remote Sens. 2020, 12(17), 2731; https://doi.org/10.3390/rs12172731 Available online: https://www.mdpi.com/2072-4292/12/17/2731
77. “A Novel Deep Forest-Based Active Transfer Learning Method for PolSAR Images” by Qin, X. et al. Remote Sens. 2020, 12(17), 2755; https://doi.org/10.3390/rs12172755 Available online: https://www.mdpi.com/2072-4292/12/17/2755	78. “Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria” by Nachappa, T. et al. Remote Sens. 2020, 12(17), 2757; https://doi.org/10.3390/rs12172757 Available online: https://www.mdpi.com/2072-4292/12/17/2757
79. “The Dimming of Lights in China during the COVID-19 Pandemic” by Elvidge, C. et al. Remote Sens. 2020, 12(17), 2851; https://doi.org/10.3390/rs12172851 Available online: https://www.mdpi.com/2072-4292/12/17/2851	80. “Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration” by Cui, S. et al. Remote Sens. 2020, 12(18), 2937; https://doi.org/10.3390/rs12182937 Available online: https://www.mdpi.com/2072-4292/12/18/2937
81. “The Effect of Climatological Variables on Future UAS-Based Atmospheric Profiling in the Lower Atmosphere” by Jacobs, A. et al. Remote Sens. 2020, 12(18), 2947; https://doi.org/10.3390/rs12182947 Available online: https://www.mdpi.com/2072-4292/12/18/2947	82. “Hyperspectral Image Classification Using Feature Relations Map Learning” by Dou, P. et al. Remote Sens. 2020, 12(18), 2956; https://doi.org/10.3390/rs12182956 Available online: https://www.mdpi.com/2072-4292/12/18/2956
83. “Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations” by Borlaf-Mena, I. et al. Remote Sens. 2020, 12(18), 3016; https://doi.org/10.3390/rs12183016 Available online: https://www.mdpi.com/2072-4292/12/18/3016	84. “Applications of Remote Sensing in Precision Agriculture: A Review” by Sishodia, R. et al. Remote Sens. 2020, 12(19), 3136; https://doi.org/10.3390/rs12193136 Available online: https://www.mdpi.com/2072-4292/12/19/3136
85. “Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK” by Taddia, Y. et al. Remote Sens. 2020, 12(19), 3144; https://doi.org/10.3390/rs12193144 Available online: https://www.mdpi.com/2072-4292/12/19/3144	86. “A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data” by Genzano, N. et al. Remote Sens. 2020, 12(19), 3232; https://doi.org/10.3390/rs12193232 Available online: https://www.mdpi.com/2072-4292/12/19/3232
87. “Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1” by Hoekman, D. et al. Remote Sens. 2020, 12(19), 3263; https://doi.org/10.3390/rs12193263 Available online: https://www.mdpi.com/2072-4292/12/19/3263	88. “Magnetospheric–Ionospheric–Lithospheric Coupling Model. 1: Observations during the 5 August 2018 Bayan Earthquake” by Piersanti, M. et al. Remote Sens. 2020, 12(20), 3299; https://doi.org/10.3390/rs12203299 Available online: https://www.mdpi.com/2072-4292/12/20/3299
89. “UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments” by Sandino, J. et al. Remote Sens. 2020, 12(20), 3386; https://doi.org/10.3390/rs12203386 Available online: https://www.mdpi.com/2072-4292/12/20/3386	90. “Evidence That Reduced Air and Road Traffic Decreased Artificial Night-Time Skyglow during COVID-19 Lockdown in Berlin, Germany” by Jechow, A. et al. Remote Sens. 2020, 12(20), 3412; https://doi.org/10.3390/rs12203412 Available online: https://www.mdpi.com/2072-4292/12/20/3412
91. “A Quantitative Framework for Analyzing Spatial Dynamics of Flood Events: A Case Study of Super Cyclone Amphan” by Hassan, M. et al. Remote Sens. 2020, 12(20), 3454; https://doi.org/10.3390/rs12203454 Available online: https://www.mdpi.com/2072-4292/12/20/3454	92. “Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada” by Amani, M. et al. Remote Sens. 2020, 12(21), 3561; https://doi.org/10.3390/rs12213561 Available online: https://www.mdpi.com/2072-4292/12/21/3561
93. “Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models” by Leite, R. et al. Remote Sens. 2020, 12(21), 3599; https://doi.org/10.3390/rs12213599 Available online: https://www.mdpi.com/2072-4292/12/21/3599	94. “Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States” by Tadesse, T. et al. Remote Sens. 2020, 12(21), 3605; https://doi.org/10.3390/rs12213605 Available online: https://www.mdpi.com/2072-4292/12/21/3605
95. “Photogrammetric 3D Model via Smartphone GNSS Sensor: Workflow, Error Estimate, and Best Practices” by Tavani, S. et al. Remote Sens. 2020, 12(21), 3616; https://doi.org/10.3390/rs12213616 Available online: https://www.mdpi.com/2072-4292/12/21/3616	96. “Land Cover Dynamics and Mangrove Degradation in the Niger Delta Region” by Nababa, I. et al. Remote Sens. 2020, 12(21), 3619; https://doi.org/10.3390/rs12213619 Available online: https://www.mdpi.com/2072-4292/12/21/3619
97. “Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data” by Hakim, W. et al. Remote Sens. 2020, 12(21), 3627; https://doi.org/10.3390/rs12213627 Available online: https://www.mdpi.com/2072-4292/12/21/3627	98. “Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery” by Rayne, L. et al. Remote Sens. 2020, 12(22), 3694; https://doi.org/10.3390/rs12223694 Available online: https://www.mdpi.com/2072-4292/12/22/3694
99. “The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)” by Yancho, J. et al. Remote Sens. 2020, 12(22), 3758; https://doi.org/10.3390/rs12223758 Available online: https://www.mdpi.com/2072-4292/12/22/3758	100. “Dark Glacier Surface of Greenland’s Largest Floating Tongue Governed by High Local Deposition of Dust” by Humbert, A. et al. Remote Sens. 2020, 12(22), 3793; https://doi.org/10.3390/rs12223793 Available online: https://www.mdpi.com/2072-4292/12/22/3793
101. “A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain” by Schneider, R. et al. Remote Sens. 2020, 12(22), 3803; https://doi.org/10.3390/rs12223803 Available online: https://www.mdpi.com/2072-4292/12/22/3803	102. “Remote Sensing of Ecosystem Structure: Fusing Passive and Active Remotely Sensed Data to Characterize a Deltaic Wetland Landscape” by Peters, D. et al. Remote Sens. 2020, 12(22), 3819; https://doi.org/10.3390/rs12223819 Available online: https://www.mdpi.com/2072-4292/12/22/3819
103. “Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings” by Lloyd, C. et al. Remote Sens. 2020, 12(23), 3847; https://doi.org/10.3390/rs12233847 Available online: https://www.mdpi.com/2072-4292/12/23/3847	104. “Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments” by Chen, W. et al. Remote Sens. 2020, 12(23), 3854; https://doi.org/10.3390/rs12233854 Available online: https://www.mdpi.com/2072-4292/12/23/3854
105. “Novel Techniques for Void Filling in Glacier Elevation Change Data Sets” by Seehaus, T. et al. Remote Sens. 2020, 12(23), 3917; https://doi.org/10.3390/rs12233917 Available online: https://www.mdpi.com/2072-4292/12/23/3917	106. “Optimizing Near Real-Time Detection of Deforestation on Tropical Rainforests Using Sentinel-1 Data” by Doblas, J. et al. Remote Sens. 2020, 12(23), 3922; https://doi.org/10.3390/rs12233922 Available online: https://www.mdpi.com/2072-4292/12/23/3922
107. “Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters” by Adam, M. et al. Remote Sens. 2020, 12(23), 3948; https://doi.org/10.3390/rs12233948 Available online: https://www.mdpi.com/2072-4292/12/23/3948	108. “Assessing the Potential Replacement of Laurel Forest by a Novel Ecosystem in the Steep Terrain of an Oceanic Island” by Devkota, R. et al. Remote Sens. 2020, 12(24), 4013; https://doi.org/10.3390/rs12244013 Available online: https://www.mdpi.com/2072-4292/12/24/4013
109. “Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach” by Ghazaryan, G. et al. Remote Sens. 2020, 12(24), 4030; https://doi.org/10.3390/rs12244030 Available online: https://www.mdpi.com/2072-4292/12/24/4030	110. “Design and Development of a Smart Variable Rate Sprayer Using Deep Learning” by Hussain, N. et al. Remote Sens. 2020, 12(24), 4091; https://doi.org/10.3390/rs12244091 Available online: https://www.mdpi.com/2072-4292/12/24/4091
111. “Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products” by Meyer, K. et al. Remote Sens. 2020, 12(24), 4096; https://doi.org/10.3390/rs12244096 Available online: https://www.mdpi.com/2072-4292/12/24/4096	112. “H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network” by Tang, G. et al. Remote Sens. 2020, 12(24), 4192; https://doi.org/10.3390/rs12244192 Available online: https://www.mdpi.com/2072-4292/12/24/4192

14 October 2022
Meet Us at the 22nd William T. Pecora Memorial Remote Sensing Symposium (Pecora 22), 23–27 October 2022, Denver, Colorado, USA

MDPI will be attending the 22nd William T. Pecora Memorial Remote Sensing Symposium (Pecora 22), held in Denver, Colorado, USA, from 23 to 27 October 2022. The booth will be available to visit from 25 to 27 October 2022.

The conference will be hosted by NASA and the USGS, with an overarching theme of Opening the Aperture to Innovation: Expanding Our Collective Understanding of a Changing Earth, which embraces both the innovations and discoveries that resulted from 50 years of Landsat Earth observations, and also current and future innovations in science and technology that are contributing to our ability to improve our understanding and better manage the Earth’s environment.

During this conference, MDPI (at booth #14) will welcome researchers from different backgrounds to visit and share their latest views and research with us.

The following MDPI journals will be represented:

If you plan on attending this conference, feel free to stop by our booth at #14. Our delegates look forward to meeting you in person to answer any questions you may have.

For more information about the conference, please see the following link: https://pecora22.org/.

10 October 2022
Remote Sensing | Editor’s Choice Articles in 2021

1. “Assessing the Behavioural Responses of Small Cetaceans to Unmanned Aerial Vehicles” by Joana, Castro et al. Remote Sens. 2021, 13(1), 156; https://doi.org/10.3390/rs13010156 Available online: https://www.mdpi.com/2072-4292/13/1/156	2. “Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery” by Jun, Li et al. Remote Sens. 2021, 13(1), 157; https://doi.org/10.3390/rs13010157 Available online: https://www.mdpi.com/2072-4292/13/1/157
3. “Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images” by Yulu, Chen et al. Remote Sens. 2021, 13(2), 208; https://doi.org/10.3390/rs13020208 Available online: https://www.mdpi.com/2072-4292/13/2/208	4. “A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula” by Youssef, Wehbe et al. Remote Sens. 2021, 13(2), 247; https://doi.org/10.3390/rs13020247 Available online: https://www.mdpi.com/2072-4292/13/2/247
5. “A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling” by Michael, Meadows et al. Remote Sens. 2021, 13(2), 275; https://doi.org/10.3390/rs13020275 Available online: https://www.mdpi.com/2072-4292/13/2/275	6. “Imaging Spectroscopy for Conservation Applications” by Megan, Seeley et al. Remote Sens. 2021, 13(2), 292; https://doi.org/10.3390/rs13020292 Available online: https://www.mdpi.com/2072-4292/13/2/292
7. “Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance” by Jingang, Zhan et al. Remote Sens. 2021, 13(3), 480; https://doi.org/10.3390/rs13030480 Available online: https://www.mdpi.com/2072-4292/13/3/480	8. “Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming” by Caiwang, Zheng et al. Remote Sens. 2021, 13(3), 531; https://doi.org/10.3390/rs13030531 Available online: https://www.mdpi.com/2072-4292/13/3/531
9. “Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa” by Isaac Kwesi, Nooni et al. Remote Sens. 2021, 13(3), 533; https://doi.org/10.3390/rs13030533 Available online: https://www.mdpi.com/2072-4292/13/3/533	10. “Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin” by Shilun, Zhou et al. Remote Sens. 2021, 13(4), 684; https://doi.org/10.3390/rs13040684 Available online: https://www.mdpi.com/2072-4292/13/4/684
11. “Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data” by Liming, He et al. Remote Sens. 2021, 13(4), 806; https://doi.org/10.3390/rs13040806 Available online: https://www.mdpi.com/2072-4292/13/4/806	12. “Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine” by Ekhi, Roteta et al. Remote Sens. 2021, 13(4), 816; https://doi.org/10.3390/rs13040816 Available online: https://www.mdpi.com/2072-4292/13/4/816
13. “Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale” by Zahra, Sharifnezhad et al. Remote Sens. 2021, 13(4), 817; https://doi.org/10.3390/rs13040817 Available online: https://www.mdpi.com/2072-4292/13/4/817	14. “Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery” by Zahra, Sharifnezhad et al. Remote Sens. 2021, 13(5), 872; https://doi.org/10.3390/rs13050872 Available online: https://www.mdpi.com/2072-4292/13/5/872
15. “Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning” by Theodora, Lendzioch et al. Remote Sens. 2021, 13(5), 907; https://doi.org/10.3390/rs13050907 Available online: https://www.mdpi.com/2072-4292/13/5/907	16. “Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning” by Fuding, Xie et al. Remote Sens. 2021, 13(5), 930; https://doi.org/10.3390/rs13050930 Available online: https://www.mdpi.com/2072-4292/13/5/930
17. “Traditional vs. Machine-Learning Methods for Forecasting Sandy Shoreline Evolution Using Historic Satellite-Derived Shorelines” by Floris, Calkoen et al. Remote Sens. 2021, 13(5), 934; https://doi.org/10.3390/rs13050934 Available online: https://www.mdpi.com/2072-4292/13/5/934	18. “Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools” by Véronique, Achard et al. Remote Sens. 2021, 13(5), 1020; https://doi.org/10.3390/rs1305102 Available online: https://www.mdpi.com/2072-4292/13/5/1020
19. “The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities” by Matthias, Schramm et al. Remote Sens. 2021, 13(6), 1125; https://doi.org/10.3390/rs13061125 Available online: https://www.mdpi.com/2072-4292/13/6/1125	20. “A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges” by Nadia, Delavarpour et al. Remote Sens. 2021, 13(6), 1204; https://doi.org/10.3390/rs13061204 Available online: https://www.mdpi.com/2072-4292/13/6/1204
21. “Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review” by Marius, Philipp et al. Remote Sens. 2021, 13(6), 1217; https://doi.org/10.3390/rs13061217 Available online: https://www.mdpi.com/2072-4292/13/6/1217	22. “Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs” by Martin, Štroner et al. Remote Sens. 2021, 13(7), 1336; https://doi.org/10.3390/rs13071336 Available online: https://www.mdpi.com/2072-4292/13/7/1336
23. “Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review” by Mercedes, Vélez-Nicolás et al. Remote Sens. 2021, 13(7), 1359; https://doi.org/10.3390/rs13071359 Available online: https://www.mdpi.com/2072-4292/13/7/1359	24. “Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission” by Christoph, Herbert et al. Remote Sens. 2021, 13(7), 1366; https://doi.org/10.3390/rs13071366 Available online: https://www.mdpi.com/2072-4292/13/7/1366
25. “The Road to Operationalization of Effective Tropical Forest Monitoring Systems” by Carlos, Portillo-Quintero et al. Remote Sens. 2021, 13(7), 1370; https://doi.org/10.3390/rs13071370 Available online: https://www.mdpi.com/2072-4292/13/7/1370	26. “Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR” by Junliang, Qiu et al. Remote Sens. 2021, 13(7), 1384; https://doi.org/10.3390/rs13071384 Available online: https://www.mdpi.com/2072-4292/13/7/1384
27. “Responses of Summer Upwelling to Recent Climate Changes in the Taiwan Strait” by Caiyun, Zhang Remote Sens. 2021, 13(7), 1386; https://doi.org/10.3390/rs13071386 Available online: https://www.mdpi.com/2072-4292/13/7/1386	28. “Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal” by Ruben, Fernandez-Beltran,et al. Remote Sens. 2021, 13(7), 1391; https://doi.org/10.3390/rs13071391 Available online: https://www.mdpi.com/2072-4292/13/7/1391
29. “The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework” by Genki, Okada et al. Remote Sens. 2021, 13(7), 1401; https://doi.org/10.3390/rs13071401 Available online: https://www.mdpi.com/2072-4292/13/7/1401	30. “Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine” by Jiwei, Li et al. Remote Sens. 2021, 13(8), 1469; https://doi.org/10.3390/rs13081469 Available online: https://www.mdpi.com/2072-4292/13/8/1469
31. “High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data” by Yufeng, Jiang et al. Remote Sens. 2021, 13(8), 1529; https://doi.org/10.3390/rs13081529 Available online: https://www.mdpi.com/2072-4292/13/8/1529	32. “Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs” by Yixin, He et al. Remote Sens. 2021, 13(8), 1547; https://doi.org/10.3390/rs13081547 Available online: https://www.mdpi.com/2072-4292/13/8/1547
33. “High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing” by Marco, Balsi et al. Remote Sens. 2021, 13(8), 1557; https://doi.org/10.3390/rs13081557 Available online: https://www.mdpi.com/2072-4292/13/8/1557	34. “On the Geopolitics of Fire, Conflict and Land in the Kurdistan Region of Iraq” by Lina, Eklund et al. Remote Sens. 2021, 13(8), 1575; https://doi.org/10.3390/rs13081575 Available online: https://www.mdpi.com/2072-4292/13/8/1575
35. “Leveraging River Network Topology and Regionalization to Expand SWOT-Derived River Discharge Time Series in the Mississippi River Basin” by Cassandra, Nickles et al. Remote Sens. 2021, 13(8), 1590; https://doi.org/10.3390/rs13081590 Available online: https://www.mdpi.com/2072-4292/13/8/1590	36. “Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing.” by Shangharsha, Thapa et al. Remote Sens. 2021, 13(8), 1597; https://doi.org/10.3390/rs13081597 Available online: https://www.mdpi.com/2072-4292/13/8/1597
37. *“Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sens.* Imagery”** by Bidroha, Basu et al. Remote Sens. 2021, 13(8), 1598; https://doi.org/10.3390/rs13081598 Available online: https://www.mdpi.com/2072-4292/13/8/1598	38. “In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a ‘Cabernet Sauvignon’ Vineyard” by Idan, Bahat et al. Remote Sens. 2021, 13(9), 1636; https://doi.org/10.3390/rs13091636 Available online: https://www.mdpi.com/2072-4292/13/9/1636
39. “Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska” by Anushree, Badola et al. Remote Sens. 2021, 13(9), 1693; https://doi.org/10.3390/rs13091693 Available online: https://www.mdpi.com/2072-4292/13/9/1693	40. “Assessing the Accuracy of ALOS/PALSAR-2 and Sentinel-1 Radar Images in Estimating the Land Subsidence of Coastal Areas: A Case Study in Alexandria City, Egypt” by Noura, Darwish et al. Remote Sens. 2021, 13(9), 1838; https://doi.org/10.3390/rs13091838 Available online: https://www.mdpi.com/2072-4292/13/9/1838
41. “GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan” by Muhammad, Tayyab et al. Remote Sens. 2021, 13(10), 1864; https://doi.org/10.3390/rs13101864 Available online: https://www.mdpi.com/2072-4292/13/10/1864	42. “Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application” by Hongxiao, Jin et al. Remote Sens. 2021, 13(10), 1866; https://doi.org/10.3390/rs13101866 Available online: https://www.mdpi.com/2072-4292/13/10/1866
43. “Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive Phragmites australis in Treatment Areas Enrolled in an Adaptive Management Program” by Colin, Brooks et al. Remote Sens. 2021, 13(10), 1895; https://doi.org/10.3390/rs13101895 Available online: https://www.mdpi.com/2072-4292/13/10/1895	44. “Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas” by Juan, López-Vinielles et al. Remote Sens. 2021, 13(10), 2008; https://doi.org/10.3390/rs13102008 Available online: https://www.mdpi.com/2072-4292/13/10/2008
45. “A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine” by Felix, Greifeneder et al. Remote Sens. 2021, 13(11), 2099; https://doi.org/10.3390/rs13112099 Available online: https://www.mdpi.com/2072-4292/13/11/2099	46. “Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case” by Stefano, Nativi et al. Remote Sens. 2021, 13(11), 2119; https://doi.org/10.3390/rs13112119 Available online: https://www.mdpi.com/2072-4292/13/11/2119
47. “UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions” by Ana I. , de Castro et al. Remote Sens. 2021, 13(11), 2139; https://doi.org/10.3390/rs13112139 Available online: https://www.mdpi.com/2072-4292/13/11/2139	48. “Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes” by Frédéric, Frappart et al. Remote Sens. 2021, 13(11), 2196; https://doi.org/10.3390/rs13112196 Available online: https://www.mdpi.com/2072-4292/13/11/2196
49. “SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality” by Kerstin, Stebel et al. Remote Sens. 2021, 13(11), 2219; https://doi.org/10.3390/rs13112219 Available online: https://www.mdpi.com/2072-4292/13/11/2219	50. “Tropical Forest Monitoring: Challenges and Recent Progress in Research” by Jennifer, Murrins Misiukas et al. Remote Sens. 2021, 13(12), 2252; https://doi.org/10.3390/rs13122252 Available online: https://www.mdpi.com/2072-4292/13/12/2252
51. “Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning” by Vaibhav, Katiyar et al. Remote Sens. 2021, 13(12), 2334; https://doi.org/10.3390/rs13122334 Available online: https://www.mdpi.com/2072-4292/13/12/2334	52. “Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery” by Paolo, Tasseron et al. Remote Sens. 2021, 13(12), 2335; https://doi.org/10.3390/rs13122335 Available online: https://www.mdpi.com/2072-4292/13/12/2335
53. “Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India” by Ponraj, Arumugam et al. Remote Sens. 2021, 13(12), 2379; https://doi.org/10.3390/rs13122379 Available online: https://www.mdpi.com/2072-4292/13/12/2379	54. “Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images” by Ziyi, Chen et al. Remote Sens. 2021, 13(13), 2524; https://doi.org/10.3390/rs13132524 Available online: https://www.mdpi.com/2072-4292/13/13/2524
55. “Assessing Repeatability and Reproducibility of Structure-from-Motion Photogrammetry for 3D Terrain Mapping of Riverbeds” by Jessica, De Marco et al. Remote Sens. 2021, 13(13), 2572; https://doi.org/10.3390/rs13132572 Available online: https://www.mdpi.com/2072-4292/13/13/2572	56. “Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve” by Bogdan, Zagajewski et al. Remote Sens. 2021, 13(13), 2581; https://doi.org/10.3390/rs13132581 Available online: https://www.mdpi.com/2072-4292/13/13/2581
57. “Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties” by Bassil, El Masri et al. Remote Sens. 2021, 13(13), 2593; https://doi.org/10.3390/rs13132593 Available online: https://www.mdpi.com/2072-4292/13/13/2593	58. “A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests” by Heather, Grybas et al. Remote Sens. 2021, 13(13), 2631; https://doi.org/10.3390/rs13132631 Available online: https://www.mdpi.com/2072-4292/13/13/2631
59. “Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series” by Stefan, Mayr et al. Remote Sens. 2021, 13(14), 2675; https://doi.org/10.3390/rs13142675 Available online: https://www.mdpi.com/2072-4292/13/14/2675	60. “The Surface Velocity Response of a Tropical Glacier to Intra and Inter Annual Forcing, Cordillera Blanca, Peru” by Andrew, Kos et al. Remote Sens. 2021, 13(14), 2694; https://doi.org/10.3390/rs13142694 Available online: https://www.mdpi.com/2072-4292/13/14/2694
61. “Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania” by Artan, Hysa et al. Remote Sens. 2021, 13(14), 2737; https://doi.org/10.3390/rs13142737 Available online: https://www.mdpi.com/2072-4292/13/14/2737	62. “Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level” by Bastien, Vandendaele et al. Remote Sens. 2021, 13(14), 2796; https://doi.org/10.3390/rs13142796 Available online: https://www.mdpi.com/2072-4292/13/14/2796
63. “Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale” by Mariella, Aquilino et al. Remote Sens. 2021, 13(14), 2835; https://doi.org/10.3390/rs13142835 Available online: https://www.mdpi.com/2072-4292/13/14/2835	64. “The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications” by Chuanpeng, Zhao et al. Remote Sens. 2021, 13(15), 2909; https://doi.org/10.3390/rs13152909 Available online: https://www.mdpi.com/2072-4292/13/15/2909
65. “Warm Arctic Proglacial Lakes in the ASTER Surface Temperature Product” by Adrian, Dye et al. Remote Sens. 2021, 13(15), 2987; https://doi.org/10.3390/rs13152987 Available online: https://www.mdpi.com/2072-4292/13/15/2987	66. “Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia” by Debbie A., Chamberlain et al. Remote Sens. 2021, 13(15), 3032; https://doi.org/10.3390/rs13153032 Available online: https://www.mdpi.com/2072-4292/13/15/3032
67. “Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia” by Debbie A., Chamberlain et al. Remote Sens. 2021, 13(15), 3032; https://doi.org/10.3390/rs13153032 Available online: https://www.mdpi.com/2072-4292/13/15/3032	68. “Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia” by Debbie A., Chamberlain et al. Remote Sens. 2021, 13(15), 3032; https://doi.org/10.3390/rs13153032 Available online: https://www.mdpi.com/2072-4292/13/15/3032
69. “Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier” by Binita, Shrestha et al. Remote Sens. 2021, 13(15), 3040; https://doi.org/10.3390/rs13153040 Available online: https://www.mdpi.com/2072-4292/13/15/3040	70. “Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery” by Deodato, Tapete et al. Remote Sens. 2021, 13(16), 3106; https://doi.org/10.3390/rs13163106 Available online: https://www.mdpi.com/2072-4292/13/16/3106
71. “Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning” by Jash R., Parekh et al. Remote Sens. 2021, 13(16), 3166; https://doi.org/10.3390/rs13163166 Available online: https://www.mdpi.com/2072-4292/13/16/3166	72. “A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information” by Ying, Zhang et al. Remote Sens. 2021, 13(16), 3297; https://doi.org/10.3390/rs13163297 Available online: https://www.mdpi.com/2072-4292/13/16/3297
73. “First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution” by Alejandro, Sánchez de Miguel et al. Remote Sens. 2021, 13(16), 3311; https://doi.org/10.3390/rs13163311 Available online: https://www.mdpi.com/2072-4292/13/16/3311	74. “Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands” by Fabio, Castaldi et al. Remote Sens. 2021, 13(17), 3345; https://doi.org/10.3390/rs13173345 Available online: https://www.mdpi.com/2072-4292/13/17/3345
75. “Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review” by Agnieszka, Kuras et al. Remote Sens. 2021, 13(17), 3393; https://doi.org/10.3390/rs13173393 Available online: https://www.mdpi.com/2072-4292/13/17/3393	76. “Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery” by Esther Shupel, Ibrahim et al. Remote Sens. 2021, 13(17), 3523; https://doi.org/10.3390/rs13173523 Available online: https://www.mdpi.com/2072-4292/13/17/3523
77. “Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets” by Carmela, Cavallo et al. Remote Sens. 2021, 13(17), 3525; https://doi.org/10.3390/rs13173525 Available online: https://www.mdpi.com/2072-4292/13/17/3525	78. “Evaluation of a Statistical Approach for Extracting Shallow Water Bathymetry Signals from ICESat-2 ATL03 Photon Data” by Heidi, Ranndal et al. Remote Sens. 2021, 13(17), 3548; https://doi.org/10.3390/rs13173548 Available online: https://www.mdpi.com/2072-4292/13/17/3548
79. “Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings” by Piyush, Pandey et al. Remote Sens. 2021, 13(18), 3595; https://doi.org/10.3390/rs13183595 Available online: https://www.mdpi.com/2072-4292/13/18/3595	80. “Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa” by Sharon E., Nicholson et al. Remote Sens. 2021, 13(18), 3609; https://doi.org/10.3390/rs13183609 Available online: https://www.mdpi.com/2072-4292/13/18/3609
81. “High-Resolution Ocean Currents from Sea Surface Temperature Observations: The Catalan Sea (Western Mediterranean)” by Jordi, Isern-Fontanet et al. Remote Sens. 2021, 13(18), 3635; https://doi.org/10.3390/rs13183635 Available online: https://www.mdpi.com/2072-4292/13/18/3635	82. “The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings” by Serena, Moretto et al. Remote Sens. 2021, 13(18), 3735; https://doi.org/10.3390/rs13183735 Available online: https://www.mdpi.com/2072-4292/13/18/3735
83. “The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests” by Sean, Reilly et al. Remote Sens. 2021, 13(19), 3810; https://doi.org/10.3390/rs13193810 Available online: https://www.mdpi.com/2072-4292/13/19/3810	84. “Wood–Leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information” by Jingqian, Sun et al. Remote Sens. 2021, 13(20), 4050; https://doi.org/10.3390/rs13204050 Available online: https://www.mdpi.com/2072-4292/13/20/4050
85. “Estimation of Plot-Level Burn Severity Using Terrestrial Laser Scanning” by Michael R., Gallagher et al. Remote Sens. 2021, 13(20), 4168; https://doi.org/10.3390/rs13204168 Available online: https://www.mdpi.com/2072-4292/13/20/4168	86. “Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception” by Micah, Russell et al. Remote Sens. 2021, 13(20), 4188; https://doi.org/10.3390/rs13204188 Available online: https://www.mdpi.com/2072-4292/13/20/4188
87. “Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing” by Randall, Bonnell et al. Remote Sens. 2021, 13(21), 4223; https://doi.org/10.3390/rs13214223 Available online: https://www.mdpi.com/2072-4292/13/21/4223	88. “Recognition of Sedimentary Rock Occurrences in Satellite and Aerial Images of Other Worlds—Insights from Mars” by Kenneth S., Edgett et al. Remote Sens. 2021, 13(21), 4296; https://doi.org/10.3390/rs13214296 Available online: https://www.mdpi.com/2072-4292/13/21/4296
89. “Opposite Spatiotemporal Patterns for Surface Urban Heat Island of Two “Stove Cities” in China: Wuhan and Nanchang” by Yao, Shen et al. Remote Sens. 2021, 13(21), 4447; https://doi.org/10.3390/rs13214447 Available online: https://www.mdpi.com/2072-4292/13/21/4447	90. “A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery” by Saüc, Abadal et al. Remote Sens. 2021, 13(22), 4547; https://doi.org/10.3390/rs13224547 Available online: https://www.mdpi.com/2072-4292/13/22/4547
91. “Application of a Convolutional Neural Network for the Detection of Sea Ice Leads” by Jay P., Hoffman et al. Remote Sens. 2021, 13(22), 4571; https://doi.org/10.3390/rs13224571 Available online: https://www.mdpi.com/2072-4292/13/22/4571	92. “Compact Thermal Imager (CTI) for Atmospheric Remote Sensing” by Dong L., Wu et al. Remote Sens. 2021, 13(22), 4578; https://doi.org/10.3390/rs13224578 Available online: https://www.mdpi.com/2072-4292/13/22/4578
93. “Comparative Study of Groundwater-Induced Subsidence for London and Delhi Using PSInSAR” by Vivek, Agarwal et al. Remote Sens. 2021, 13(23), 4741; https://doi.org/10.3390/rs13234741 Available online: https://www.mdpi.com/2072-4292/13/23/4741	94. “A Self-Adaptive Method for Mapping Coastal Bathymetry On-The-Fly from Wave Field Video” by Matthijs, Gawehn et al. Remote Sens. 2021, 13(23), 4742; https://doi.org/10.3390/rs13234742 Available online: https://www.mdpi.com/2072-4292/13/23/4742
95. “Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data” by Francesca, Cigna et al. Remote Sens. 2021, 13(23), 4800; https://doi.org/10.3390/rs13234800 Available online: https://www.mdpi.com/2072-4292/13/23/4800	96. “Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information” by Amal, Chakhar et al. Remote Sens. 2021, 13(24), 4968; https://doi.org/10.3390/rs13244968 Available online: https://www.mdpi.com/2072-4292/13/24/4968
97. “QDC-2D: A Semi-Automatic Tool for 2D Analysis of Discontinuities for Rock Mass Characterization” by Lidia, Loiotine et al. Remote Sens. 2021, 13(24), 5086; https://doi.org/10.3390/rs13245086 Available online: https://www.mdpi.com/2072-4292/13/24/5086	98. “Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones” by Lucrezia, Ricciardulli et al. Remote Sens. 2021, 13(24), 5110; https://doi.org/10.3390/rs13245110 Available online: https://www.mdpi.com/2072-4292/13/24/5110

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