国内叶片高光谱研究的知识图谱与发展态势分析

Knowledge Mapping and Development Trends of Leaf Hyperspectral Research in China

关键词:  叶片  高光谱技术  反演  文献计量学  机器学习

Key words:  Leaf  Hyperspectral Imaging  Parameter Retrieval  Bibliometric Analysis  Machine Learning

摘要:

【目的】为了系统梳理国内叶片高光谱研究的发展脉络与研究热点,弥补现有文献在整体整合与研究趋势分析方面的不足,为该领域提供整体性知识整合与研究趋势分析提供思路和基础,【方法】本研究基于中国知网(CNKI)数据库2006-2025年891篇文献,采用文献计量学与知识图谱方法,对发文特征、期刊分布、作者与机构合作及关键词演化等进行综合分析。【结果】结果表明：(1)叶片高光谱研究经历快速增长、结构调整与成熟稳定三个阶段,核心期刊呈集聚特征；(2)作者与机构以小团队为主,跨区域协同需要加强；(3)研究重点集中于生理参数反演、光谱处理与智能算法、胁迫监测及基础理论方法；(4)研究热点由单一指标向多指标、由传统方法向智能算法转变,无人机与深度学习成为前沿方向。【结论】综上,领域整体呈现技术驱动与应用导向并行的发展特征。本研究可为解决该领域文献分散、脉络不清的问题提供参考,并为未来相关研究选题布局与方法拓展提供文献计量学依据。

Abstract:

[Purpose] This study systematically sorts out the development context and research hotspots of leaf hyperspectral research in China, compensates for the shortcomings of existing literatures in comprehensive integration and research trend analysis, and provides ideas and a foundation for integrated knowledge collation and trend analysis in this field. [Method] Based on 891 papers retrieved from the China National Knowledge Infrastructure (CNKI) database published from 2006 to 2025, bibliometrics and knowledge mapping methods were adopted to comprehensively analyze publication characteristics, journal distribution, author and institutional collaboration, as well as the evolution of keywords. [Result] The results show that: (1) Research on leaf hyperspectra has gone through three stages: rapid growth, structural adjustment, maturity and stability, and the core journals showed agglomeration characteristics; (2) Research is mainly carried out by small teams, and cross-regional cooperation needs to be further strengthened; (3) The research focuses lie in physiological parameter inversion, spectral processing and intelligent algorithms, stress monitoring, and basic theoretical methodologies; (4) Research hotspots have shifted from single indicators to multi-indicator systems and from traditional analytical methods to intelligent algorithms, with unmanned aerial vehicles and deep learning emerging as cutting-edge directions. [Conclusion] In general, the field develops under the dual drivers of technological innovation and practical application. This study can serve as a reference to address the fragmentation and unclear developmental context of relevant literatures, and provide bibliometric support for topic selection and methodological expansion of future research.