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

• Published in: Atmosphere 2024-05, Vol.15 (5), p.598
• Main Authors: Cui, Jiawei, Li, Juan, Gu, Xingfa, Zhang, Wenhao, Wang, Dong, Sun, Xiuling, Zhan, Yulin, Yang, Jian, Liu, Yan, Yang, Xiufeng
• Format: Article
• Language: English
• Subjects: Academic disciplines; Artificial satellites in remote sensing; Bibliographic records; Bibliometrics; Citation indexes; Climatic conditions; Data analysis; Data collection; Data integration; Deep learning; Forecasts and trends; Keywords; Long short-term memory; Methods; multi-temporal synthesis; network analysis; Remote sensing; Research facilities; Research institutions; Satellites; Science; Spatial analysis; spatiotemporal fusion; Surface dynamics; Temporal resolution; Trends; Web of Science
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos15050598

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.