利用文献计量学工具全面分析 1991 至 2023 年时空融合情况

Atmosphere Pub Date : 2024-05-14 DOI:10.3390/atmos15050598
Jiawei Cui, Juan Li, Xingfa Gu, Wenhao Zhang, Dong Wang, Xiuling Sun, Yulin Zhan, Jian Yang, Yan Liu, Xiufeng Yang
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引用次数: 0

摘要

由于预算和传感器技术的限制,单个传感器无法同时提供高空间分辨率和高时间分辨率的观测图像。为了解决上述问题,人们提出了时空融合(STF)方法,该方法被证明是监测地表动态不可或缺的工具。关于 STF 方法的系统综述相对较少。文献计量学是一种有价值的科学文献分析方法,但尚未应用于 STF 方法的综合分析。因此,在本文中,我们利用文献计量学和科学制图法对 Web of Science 1991 年至 2023 年的 2967 篇引文数据进行了计量分析,涵盖了 STF、数据融合、多时态分析和空间分析等主题。文献分析结果表明,在研究期间,论文数量呈现出由慢到快的增长趋势,但到 2023 年,论文数量明显减少。中国的研究机构(1059 篇)和美国的研究机构(432 篇)是该领域的前两位贡献者。过去三年中,"哨兵"、"深度学习"(DL)和 "LSTM"(长短期记忆)等关键词出现的频率最高。未来,遥感时空融合研究可以更多地解决异质地貌和气候条件的限制,以提高融合图像的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools
Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords “Sentinel”, “deep learning” (DL), and “LSTM” (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images’ accuracy.
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