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Volume 15
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10.3390/atmos15050598
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Review

Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools

by
Jiawei Cui
1,2,
Juan Li
1,*,
Xingfa Gu
1,
Wenhao Zhang
2,3,
Dong Wang
1,
Xiuling Sun
2,3,
Yulin Zhan
1,
Jian Yang
1,
Yan Liu
1 and
Xiufeng Yang
2,3
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
3
Hebei Collaborative Innovation Center for Aerospace Remote Sensing Information Processing and Application, Langfang 065000, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(5), 598; https://doi.org/10.3390/atmos15050598
Submission received: 7 March 2024 / Revised: 10 May 2024 / Accepted: 13 May 2024 / Published: 14 May 2024
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Abstract

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.
Keywords: bibliometrics; spatiotemporal fusion; remote sensing; multi-temporal synthesis; network analysis; Web of Science bibliometrics; spatiotemporal fusion; remote sensing; multi-temporal synthesis; network analysis; Web of Science

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MDPI and ACS Style

Cui, J.; Li, J.; Gu, X.; Zhang, W.; Wang, D.; Sun, X.; Zhan, Y.; Yang, J.; Liu, Y.; Yang, X. Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools. Atmosphere 2024, 15, 598. https://doi.org/10.3390/atmos15050598

AMA Style

Cui J, Li J, Gu X, Zhang W, Wang D, Sun X, Zhan Y, Yang J, Liu Y, Yang X. Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools. Atmosphere. 2024; 15(5):598. https://doi.org/10.3390/atmos15050598

Chicago/Turabian Style

Cui, Jiawei, Juan Li, Xingfa Gu, Wenhao Zhang, Dong Wang, Xiuling Sun, Yulin Zhan, Jian Yang, Yan Liu, and Xiufeng Yang. 2024. "Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools" Atmosphere 15, no. 5: 598. https://doi.org/10.3390/atmos15050598

APA Style

Cui, J., Li, J., Gu, X., Zhang, W., Wang, D., Sun, X., Zhan, Y., Yang, J., Liu, Y., & Yang, X. (2024). Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools. Atmosphere, 15(5), 598. https://doi.org/10.3390/atmos15050598

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