Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020

Tong Li; Lizhen Cui; Zhihong Xu; Ronghai Hu; Pawan K. Joshi; Xiufang Song; Li Tang; Anquan Xia; Yanfen Wang; Da Guo; Jiapei Zhu; Yanbin Hao; Lan Song; Xiaoyong Cui

doi:10.3390/rs13071279

Remote Sensing (Mar 2021)

Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020

Tong Li,
Lizhen Cui,
Zhihong Xu,
Ronghai Hu,
Pawan K. Joshi,
Xiufang Song,
Li Tang,
Anquan Xia,
Yanfen Wang,
Da Guo,
Jiapei Zhu,
Yanbin Hao,
Lan Song,
Xiaoyong Cui

Affiliations

Tong Li: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Lizhen Cui: Yanshan Earth Critical Zone and Surface Fluxes Research Station, Chinese Academy of Sciences, Beijing 100049, China
Zhihong Xu: School of Environment and Science, Griffith University, Nathan, Brisbane, QLD 4111, Australia
Ronghai Hu: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Pawan K. Joshi: Special Centre for Disaster Research, School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India
Xiufang Song: National Science Library, Chinese Academy of Sciences, Beijing 100190, China
Li Tang: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Anquan Xia: Yanshan Earth Critical Zone and Surface Fluxes Research Station, Chinese Academy of Sciences, Beijing 100049, China
Yanfen Wang: Yanshan Earth Critical Zone and Surface Fluxes Research Station, Chinese Academy of Sciences, Beijing 100049, China
Da Guo: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Jiapei Zhu: Yanshan Earth Critical Zone and Surface Fluxes Research Station, Chinese Academy of Sciences, Beijing 100049, China
Yanbin Hao: Yanshan Earth Critical Zone and Surface Fluxes Research Station, Chinese Academy of Sciences, Beijing 100049, China
Lan Song: School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Xiaoyong Cui: Yanshan Earth Critical Zone and Surface Fluxes Research Station, Chinese Academy of Sciences, Beijing 100049, China

DOI: https://doi.org/10.3390/rs13071279
Journal volume & issue: Vol. 13, no. 7
p. 1279

Abstract

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Grassland remote sensing (GRS) is an important research topic that applies remote sensing technology to grassland ecosystems, reflects the number of grassland resources and grassland health promptly, and provides inversion information used in sustainable development management. A scientometrics analysis based on Science Citation Index-Expanded (SCI-E) was performed to understand the research trends and areas of focus in GRS research studies. A total of 2692 papers related to GRS research studies and 82,208 references published from 1980 to 2020 were selected as the research objects. A comprehensive overview of the field based on the annual documents, research areas, institutions, influential journals, core authors, and temporal trends in keywords were presented in this study. The results showed that the annual number of documents increased exponentially, and more than 100 papers were published each year since 2010. Remote sensing, environmental sciences, and ecology were the most popular Web of Science research areas. The journal Remote Sensing was one of the most popular for researchers to publish documents and shows high development and publishing potential in GRS research studies. The institution with the greatest research documents and most citations was the Chinese Academy of Sciences. Guo X.L., Hill M.J., and Zhang L. were the most productive authors across the 40-year study period in terms of the number of articles published. Seven clusters of research areas were identified that generated contributions to this topic by keyword co-occurrence analysis. We also detected 17 main future directions of GRS research studies by document co-citation analysis. Emerging or underutilized methodologies and technologies, such as unmanned aerial systems (UASs), cloud computing, and deep learning, will continue to further enhance GRS research in the process of achieving sustainable development goals. These results can help related researchers better understand the past and future of GRS research studies.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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