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

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 7; p. 1279
• Main Authors: Li, Tong, Cui, Lizhen, Xu, Zhihong, Hu, Ronghai, Joshi, Pawan K., Song, Xiufang, Tang, Li, Xia, Anquan, Wang, Yanfen, Guo, Da, Zhu, Jiapei, Hao, Yanbin, Song, Lan, Cui, Xiaoyong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2021
• Subjects: Bibliometrics; Big Data; Citation analysis; CiteSpace; Cloud computing; Deep learning; Ecology; Environmental science; grassland; Grasslands; Interdisciplinary subjects; network analysis; Quantitative analysis; R&D; Remote sensing; Research & development; Research methodology; research progress; Science; Scientometrics; scientometrics analysis; Software; Sustainable development; Trends; Unmanned aerial vehicles; Visualization; United States > US; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13071279

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.