**2. Data and Methods**

#### *2.1. Retrieval Strategy*

Articles with flash floods and remote sensing as research topics that were published from 2000 to October 2020 were retrieved, these articles were included in the Web of Science (WOS) Core Collection. A total of 248 articles were retrieved.

#### *2.2. Literature Visual Analysis*

CiteSpace is software developed by Chaomei Chen with an information visualization function based on the Java environment. Through keywords, authors, institutions, etc., one can perform visual analysis and generate various knowledge graphs, which can be used to show current research hotspots and trends to help people better understand research in a certain field. To date, many people have used CiteSpace for data mining and visual analysis [46–48]. In this paper, a total of 248 articles were included to generate citation analysis reports (such as node size, keyword co-occurrence, time zone view, etc.) by CiteSpace 5.6. R4(64-bit).


#### *2.3. Explanation of Visual Map Icons in Maps*


#### **3. Results**

*3.1. Citation Frequency of Remote Sensing and GIS Applied to Flash Flood*

From 2000 to 2020, the citation frequency of articles on remote sensing used in flash floods increased year by year. Therefore, interest in research hotspots related to flash floods using remote sensing is increasing year by year (Figure 1).

**Figure 1.** Trends in the citation frequency of the 248 included articles from 2000 to 2020.

Representative examples of highly cited articles: the 5 most cited articles from 248 articles are selected, listed in Table 1. These articles mainly involve rainfall estimation, methods of determining flood occurrence, and estimating the risk of flash floods.

The first three of these five highly cited articles are review articles. They are about radar rainfall estimation, flash flood warning systems, and flash flood forecasting modeling technology, which are all around the key issues of flash floods. The last two articles both researched a specific watershed in Egypt and used models to predict locations that are vulnerable to flash floods. The difference lies in the different models used by the two. The research of Youssef et al. [49] was conducted in a GIS environment. The amount of data is greater, the research scope is wider, and the parameters used are greater. The research of Foody et al. [50] has less data and fewer parameters, so the results obtained are less and simpler compared to Youssef's research.

The most cited articles are usually landmarks, they are groundbreaking or forwardlooking. The study performed by Krajewski et al. [51] proposed some suggestions, including the establishment of long-term monitoring and verification stations to provide detailed information about precipitation, and believed that radar rain products have great development potential in flash flood forecasting. In recent years, radar has been widely used in precipitation estimation [7,51,52], confirming the prediction of Krajewski et al. [51] One of the scientific advances proposed by Borga et al. [53] is integrating multiple early warning methods, which has not been achieved until now. Whether the flash flood forecasting methods proposed in each research area can be realized in other areas still needs further discussion and verification [54]. For areas with similar topography, climate, soil, geology, land use, land cover, etc., it seems possible to use the same method for risk assessment [41]. Since cities have large impervious areas and a large population, once flash floods occur, they will cause many economic losses and casualties. Therefore, special attention should be given to flash flood forecasts in urban areas by Hapuarachchi et al. [7].


**Table 1.** Hot spot analysis of highly cited articles, classified from 248 articles using remote sensing to study flash floods.

In Youssef's research [49], GIS software was used to process remote sensing data, as well as to address terrain and field data to assess the risk of flash floods. Morphometrics were used to estimate the risk level of flash floods in the research basin. In subsequent research, there were a large number of articles that referred to the method in this article for the evaluation of flash flood hazards [1,41,55,56].

The research of Foody was published in 2004 [50]. The study used hydrological models to predict locations that are particularly vulnerable to flash floods under limited data conditions. This result has promoted the development of related fields and has guiding significance for subsequent research on the use of hydrological models to forecast flash floods [25,57–60].
