摘要
为分析国内外径流量遥感反演领域发展趋势、合作关系和研究现状,本研究基于中国知网(CNKI)和Web of Science(WOS)数据库,分别以“遥感径流”“径流量反演”“遥感数据”“径流量”和“runoff inversion”“remote sensing”“river discharge”为关键词检索并筛选,共得到2000—2023年1726篇径流量遥感反演领域文献,利用CiteSpace软件从文献发表时间、机构、国家、作者、期刊和关键词6个方面进行分析。结果表明:1)2000年以来,CNKI与WOS数据库文献数量均呈先低速后高速增长态势,中文文献发文量突变点为2015年,英文文献发文量突变点为2012年。2)美国、中国和法国在该领域发文量位居前3,其中中国科学院、加利福尼亚理工学院为主要发文机构;Bjerklie D M和Smith L C为高共被引作者;Remote Sensing of Environment和Journal of Hydrology为高共被引期刊。3)2个数据库的关键词聚类均可归纳为6个方向,且2000年以来发文量均有增加,其中方向5(基于长时间序列数据训练深度学习模型进行反演径流量)发文量增加最为明显,2019年后发文量远高于其他方向,成为当下径流量遥感反演领域研究热点。4)CNKI数据库文献研究热点发展分为2个阶段,第一阶段(2000—2015年)为传统水文模型研究期,主要聚焦于传统物理机制的水文模型研究;第二阶段(2016—2023年)为技术融合快速发展期,主要通过引入国际上较先进的数理模型进行研究。WOS数据库研究热点发展也分为2个阶段,第一阶段(2000—2012年)为多模型探索期,主要尝试通过多元模型反演径流量;第二阶段为(2013—2023年)融合发展期,主要进行数理模型与多方法融合研究,且从研究内容第一次出现的时间上看,英文文献发表时间普遍早于中文文献。未来研究方向应聚焦于多源数据融合、模型改进优化、流域特性反演方法适配和影响机制探究等。
To analyze the development trends,cooperation relationships and research status of remote sensing inversion of runoff at home and abroad.this study is based on the databases of CNKI and WOS,the keywords“remote sensing runoff”“runoff retrieval”“remote sensing data”,“runoff”and“runoff inversion”“,remote sensing”and“river discharge”were searched and screend,respectively.A total of 1726 literatures in the field of runoff remote sensing inversion from 2000 to 2023 were obtained.The CiteSpace software was used to analyze the literature in terms of six aspects:publication time,institution,country,author,periodicals and keywords.The results showed that:1)Since 2000,the number of domestic and foreign literature has shown a trend of slow growth followed by high growth.The abrupt change in the number of Chinese literature publications occurred in 2015,and the abrupt change in the number of English literature publications occurred in 2012.2)The United States,China and France ranked the top three in terms of the number of publications in this field,of which the Chinese Academy of Sciences and the California Institute of Technology as the main publishing institutions.The authors of the study,Bjerklie D M and Smith L C,are the most highly cited authors.Remote Sensing of Environment and Journal of Hydrology journals are highly cited journals.3)Keywords clustering can be categorized into six directions,and the number of publications has increased since 2000,of which direction 5(training deep learning models for inverting runoff volume based on long time series data)has seen the most significant increase in publications,and since 2019,the number of publications has far exceeded other directions,becoming a research hotspot in the field of runoff remote sensing inversion.4)The development of research hotspots in CNKI database can be divided into two stages.The first phase(2000-2015)is the traditional hydrological modelling period,which focuses on the hydrological modelling of the traditional physical mechanism,and the second phase(2016-2023)is the period of rapid development of technological convergence,which introduces the more advanced mathematical and physical models in the international field for inversion.The development of research hotspots in the WOS is also divided into two phases,the first phase(2000-2012)is the multi-model exploration period,mainly trying to multivariate model inversion of runoff,and the second phase is the(2013-2023)fusion development period,mainly focusing on out the research on the fusion of mathematical and physical models and multi-methods,and from the point of view of the first appearance of the research content,the English literature tends to be earlier than the Chinese literature.Future research should focus on the fusion of multi-source data,model improvement and optimization,adaptation of basin characteristics inversion methods,and investigation of impact mechanisms.
作者
关昊哲
冶运涛
顾晶晶
周政
曹引
蒋云钟
GUAN Haozhe;YE Yuntao;GU Jingjing;ZHOU Zheng;CAO Yin;JIANG Yunzhong(State Key Laboratory of Watershed Water Cycle Modeling and Regulation,China Institute of Water Resources and Hydropower Research,Beijing 100038,China)
出处
《中国农业大学学报》
北大核心
2026年第2期129-140,共12页
Journal of China Agricultural University
基金
国家重点研发计划(2023YFC3209302-03)
国家自然科学基金项目(52279031,52309040)
北京市自然科学基金项目(JQ21029)。
