*3.2. Keyword Co-Occurrence*

CiteSpace software was used to analyze the 248 selected articles, the software's own remove duplicates (WOS) function was used to remove duplicates; one was selected for time slicing, the keyword node type was used, and pathfinder and pruning sliced were selected for the pruning option to improve the network readability networks. After merging words with the same meaning and deleting meaningless words, keywords with a frequency of more than five times are retained, and a keyword co-occurrence map was generated, as shown in Figure 2. The list of the frequency of keywords that appear more than 10 times is shown in Figure 3.

In Figure 2, the larger the word, the more frequently it appears. In Figure 2, it can be seen that, in addition to remote sensing and flash floods, the size of GIS, model, rainfall, risk, and other words is larger. The GIS is a method discovered by Correia et al. [61] that can be used to integrate and investigate information about flood disasters and is widely used to reproduce the research results of different cases. This is not surprising. As the tool most frequently used in the research of flash floods using remote sensing data is GIS, GIS can provide powerful tools for risk assessment and can integrate a variety of remote sensing data in the GIS environment. To forecast flash floods and evaluate the risk

value of flash flood disasters, a variety of models are generated and frequently used. It is worth noting that, because the digital elevation model (DEM) can be used for hydrological analysis such as rainfall analysis, inundation analysis, and water system network analysis, the DEM is the most important factor in the hydrological model used to draw the flash flood disaster index of the study area [62]. The susceptibility of hydraulic modeling results was influenced by DEM accuracy [63]. DEM is often used in the study of flash floods [64].

**Figure 2.** The keyword co-occurrence map.

**Figure 3.** Keywords with more than 10 occurrences in 248 articles.

The most commonly used remote sensing data are Sentinel-1 and radar. People are paying more attention to flash floods in Egypt. Risk appears more frequently, indicating that there are more articles on the evaluation of the risk value of flash floods, indicating that more people are concerned about where flash floods may occur in order to take preventive measures in advance.

#### *3.3. A Time Zone Map of Keywords*

To further explore the dynamic evolution of flash flood research hotspots using remote sensing from 2000 to 2020, and to understand the key points of international research in different periods based on the generated keyword co-occurrence map, we use CiteSpace software and select "Time Zone View" in "Layout" to generate a keyword time zone map, as shown in Figure 4. (Selected keywords that appear more than five times are displayed in the figure).

The abscissa corresponding to the keyword in the figure indicates the year when it first appeared. The node colors of red, orange, yellow, green, blue, and purple are 2000 to 2020. The color of the line between the nodes indicates the year when the two keywords first appeared at the same time. Similar to the color of the node, the line colors of red, orange, yellow, green, blue, and purple correspond to the years 2000 to 2020.

From Figure 4, we can see that, in the study of flash floods using remote sensing, radar data have been used in the study of flash floods in 2000 or even before 2000, and since 2018, Sentinel-1 has been frequently used in research, the cumulative number of Sentinel-1 appearing in the article has reached six times in just three years. The images collected by Sentinel-1 can be used to obtain high-resolution images, regardless of weather conditions, so that they can be used to monitor floods. The synthetic aperture radar (SAR) data were collected from the Sentinel-1 sensor. Using SAR images, it can be used to distinguish water from other objects. Therefore, Sentinel-1 has often been used in the research of flash floods in recent years [65–68].

**Figure 4.** A time zone map of keywords.

The appearance of keywords in the picture can be roughly divided into five stages. The first stage was from 2000 to 2005. Keywords such as GIS, model, rainfall, runoff, etc. that appeared in this stage are still hotspots of current research. GIS as a research tool is effective and reliable. Research using models is also a common method. Among the factors that cause flash floods, rainfall is the most common cause of flash floods and has been studied the most. Soil erosion caused by flash floods has always been a concern. The second stage is from 2006 to 2009. During this stage, few new research hotspots appeared, and the research hotspots were mainly focused on the previous stage. The third stage is from 2010 to 2013. In this stage, climate change, risk, catchment, Egypt, etc. have become new research hotspots. People are beginning to pay attention to the increasing frequency of flash floods due to the frequent occurrence of extreme weather such as climate change and heavy rains [69,70]. Egypt is a typical area suffering from severe flash floods [71–74], this result is the same as Figure 2, but since 2010, the use of remote sensing to study flash floods has become popular. The fourth stage is from 2014 to 2015. There are few hot words in this stage, and the research hotspots are still based on previous research hotspots. In the fifth stage, from 2016 to 2019, research on basins increased, and the use of morphometric analysis and Sentinel-1 greatly promoted research on flash floods [65–68,75–77].
