TY - JOUR
T1 - A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020
AU - Yan, Kai
AU - Zou, Dongxiao
AU - Yan, Guangjian
AU - Fang, Hongliang
AU - Weiss, Marie
AU - Rautiainen, Miina
AU - Knyazikhin, Yuri
AU - Myneni, Ranga B.
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China [grant number 41901298], the Open Fund of State Key Laboratory of Remote Sensing Science [grant number OFSLRSS201924], the Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences [grant number 2018LDE002], and the Fundamental Research Funds for the Central Universities [grant number 2652018031].
Publisher Copyright:
Copyright © 2021 Kai Yan et al.
PY - 2021
Y1 - 2021
N2 - The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000. This review intends to summarize the history, development trends, scientific collaborations, disciplines involved, and research hotspots of these products. Its aim is to intrigue researchers and stimulate new research direction. Based on literature data from the Web of Science (WOS) and associated funding information, we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis (SNA) methods. We drew the following conclusions: (1) research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9% in the number of publications. (2) Researchers from China and the USA are the backbone of this research area, among which the Chinese Academy of Sciences (CAS) is the core research institution. (3) Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology. (4) Ecology, crop production estimation, algorithm improvement, and validation are the hotspots of these studies. (5) Broadening the research field, improving the algorithms, and overcoming existing difficulties in heterogeneous surface, scale effects, and complex terrains will be the trend of future research. Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.
AB - The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000. This review intends to summarize the history, development trends, scientific collaborations, disciplines involved, and research hotspots of these products. Its aim is to intrigue researchers and stimulate new research direction. Based on literature data from the Web of Science (WOS) and associated funding information, we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis (SNA) methods. We drew the following conclusions: (1) research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9% in the number of publications. (2) Researchers from China and the USA are the backbone of this research area, among which the Chinese Academy of Sciences (CAS) is the core research institution. (3) Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology. (4) Ecology, crop production estimation, algorithm improvement, and validation are the hotspots of these studies. (5) Broadening the research field, improving the algorithms, and overcoming existing difficulties in heterogeneous surface, scale effects, and complex terrains will be the trend of future research. Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.
UR - http://www.scopus.com/inward/record.url?scp=85106904725&partnerID=8YFLogxK
U2 - 10.34133/2021/7410921
DO - 10.34133/2021/7410921
M3 - Review Article
AN - SCOPUS:85106904725
SN - 2097-0064
VL - 2021
JO - Journal of Remote Sensing
JF - Journal of Remote Sensing
M1 - 7410921
ER -