Eddy Covariance: A Scientometric Review (1981-2018)

TL;DR

This scientometric review comprehensively analyzes the development, research trends, collaborations, and future prospects of eddy covariance (EC) studies from 1981 to 2018, highlighting key contributors, hotspots, and barriers.

Contribution

It provides a detailed bibliometric and network analysis of EC research, revealing knowledge structures, collaboration patterns, and future challenges in data sharing and model development.

Findings

Identified key countries, institutions, and authors in EC research.

Mapped research hotspots and their evolution over time.

Evaluated the performance of the FLUXNET collaboration network.

Abstract

The history of eddy covariance (EC) measuring system could be dated back to 100 years ago, but it was not until the recent decades that EC gains popularity and being widely used in global change ecological studies, with explosion of related work published in papers from various journals. Investigating 8297 literature related with EC from 1981 to 2018, we make a comprehensive and critical review of scientific development of EC from a scientometric perspective. First, the paper outlines general bibliometric statistics, including publication number, country contribution, productive institutions, active authors, journal distribution, highly cited articles and fund support, to provide an informative picture of EC studies. Second, research trends are revealed by network visualization and modeling based on keyword analysis, from where we could discover the knowledge structure of EC and detect the research focus and hotspots transitions at different periods. Third, collaboration in EC research community has been explored. FLUXNET is the largest global network uniting EC researchers, here we have quantified and evaluated its performance by using bibliometric indicators of cooperation and citation. Specific discussions have been given to the historical development of EC, including technical maturation and application promotion. Considering the current barrier for collaboration, the review closes by analyzing the reasons hindering data sharing and makes a prospect of new models for data-intensive collaboration in the future.