*3.4. A Map of Burst Keywords from 248 Articles*

Figure 5 lists the five keywords with the highest emergence intensity. From the figure, we can see that there was no keyword emergence before 2011, which means that before 2011, there were no issues that received more attention in the research on flash floods using remote sensing data. Egypt has the longest burst time. The increase in research on Egypt from 2011 to 2015 shows that there were more flash flood disasters in Egypt, and many places were affected by flash floods. The keyword with the strongest burst intensity is basin. The emergence time is 2017, and the stop time is 2020. This shows that since 2017, people's attention to the basin has increased. The burst time closest to the current burst keyword is uncertainty. The existing hydrological forecast chain is affected by many uncertain factors [78]. In recent years, with the continuous development of remote sensing technology, the pursuit of nearly real-time accurate simulation is about to become a global standard to ensure improved flash flood forecast and warning systems and ensure that models can be used in more areas and reduce the uncertainty of the model's output value. N. S. Bartsotas, Rouya Hdeib, and Hossein Mojaddadi Rizeei reduced the error of satellite precipitation estimation by optimizing algorithms and calibrating models [79–81].


**Figure 5.** Keywords with the strongest citation bursts in 248 articles.

### *3.5. Co-cited Results of Cited References*

Clustering analysis of the cited references of 248 articles published from 2000 to 2020, the results can be divided into 8 clusters, using A (Abstract) to extract nominal terms to name the clusters. The results are shown in Figure 6: #0 eastern desert, #1 debris flow, #2 Najran area, #5 flood susceptibility map, #6 flash-flood predictor, #8 ground radar, and #9 flood susceptibility map.

The color of each cluster block represents the year when the co-citation relationship first appeared in each cluster. The colors of cluster blocks range from gray to purple, blue, green, yellow, and red, representing the years from 2000 to 2020. The color of each cluster block indicates the year when the co-citation relationship first appeared in each cluster. The connecting line between the nodes indicates the path of the reference. The connecting lines between the nodes indicate the path being cited, and the color of each line indicates the time when it was first cited. A few references are highly co-cited, so here, we set a threshold to show them.

The timeline map reveals changes in reference co-citations over time. According to the generated cluster diagram (Figure 6), a timeline map of cited references can be generated by the layout function. The *Y*-axis is defined as the cluster name defined by A (Abstract), and the *X*-axis is defined as the year of publication. The timeline chart shows the time span and research progress of the development and evolution of the eight clusters, as shown in Figure 7.

**Figure 6.** Reference co-citation network for the 248 included articles (clustered according to index terms).

**Figure 7.** Timeline view of the citation trends identified in the 248 included articles.

In the timeline view, the references of the same cluster are placed on the same horizontal line. In the timeline view, the number of references in each cluster can be clearly seen. More references in the cluster representing the cluster are more important. The cluster labelling on the right shows the research hotspot category associated with each reference.

The circle in the figure represents the circle of the citation directory tree, the color at the center of the circle represents the year of the reference publication (the color corresponds to the year at the top of the view in the figure), and the size of the circle represents the frequency of citations. The cluster label name on the right indicates the research hotspot category related to the references in the cluster. Gray represents the earlier publication year, and red represents the most recent publication year. The longer color line segment indicates that the citation has a large time span, and its research hotspot is the subject that people have paid attention to for a long time. The red color at the outermost layer of the node's annual ring indicates that the citation frequency has increased rapidly or continues to increase rapidly.

Figure 7 shows four highly cited landmark articles, which are authoritative studies in the corresponding clusters. Research on the eastern desert took a long time, and a landmark article appeared in this cluster: Youssef et al. [49], which has been introduced before.

Elkhrachy et al. [1] were co-cited 14 times (Figure 8a,b), which is the most co-cited article among 248 articles. Tehrany et al. [80] were co-cited 10 times (Figure 8c,d), which is the second most co-cited article among 248 articles. Masoud Bakhtyari Kia et al. were co-cited six times (Figure 8e,f), and they are very representative articles in the field of flood susceptibility maps. Elkhrachy et al. [1] provided an accurate assessment by using SPOT and SRTM DEMs data. The analytical hierarchical process (AHP) was used to determine the relative impact weight of flash flood causative factors to obtain the composite flood hazard index (FHI). Finally, all the used data were integrated into ArcMap to generate the final flood disaster map of the study area. In previous studies, researchers have proposed many methods to perform flood susceptibility mapping, but these methods have certain shortcomings. To find a more accurate method, Tehrany et al. [80] proposed a new integration method that combines weights-of-evidence (WoE) and the support vector machine (SVM) model, not only solving the shortcomings of WoE but also enhancing the performance of SVM. The results are compared with the results obtained by using WoE and SVM alone, and the results obtained through integration are more ideal. Kia et al. [64] used artificial neural network (ANN) technology, which is one of the machine learning methods, to develop a flood model using various flood causative factors (including slope, flow accumulation, rainfall, soil, elevation, geology, and land use) to model and simulate flood-prone areas in the southern part of peninsular Malaysia. The ANN is more robust than other statistical and deterministic methods and has high computational efficiency. However, when using ANN modeling, there may be disadvantages such as errors caused by the length of the dataset.

Figure 8a,c,e are the pennant diagrams of Elkhrachy et al. [1], Tehrany et al. [80], and Kia et al. [64], respectively, which can be used to view the information for the references directly connected to a node. These figures show the distribution of articles that have a citation relationship with these articles. The closer the position of the reference article is to the bottom, the more times it has been cited. Figure 8b,d,f show the time trends, respectively, and show the number of times that Elkhrachy et al. [1], Tehrany et al. [80], and Kia et al. [64] were co-cited.

Combining Figures 7 and 8, we can see that Elkhrachy et al. [1], Tehrany et al. [80], and Kia et al. [64] were cited twice in 2016, and then, in 2019–2020, the number of citations increased suddenly, indicating that in 2016 and from 2019 to 2020, the research on flood susceptibility maps was relatively concentrated.

**Figure 8.** Co-citation status of four representative articles in the #6 flood susceptibility map in the timeline view. (**a**,**c**,**e**) are the pennant diagrams of Elkhrachy et al. [1], Tehrany et al. [80], and Kia et al. [64], respectively, which can be used to view the information for the refer-ences directly connected to a node. (**b**,**d**,**f**) show the time trends, respectively, and show the num-ber of times that Elkhrachy et al. [1], Tehrany et al. [80], and Kia et al. [64] were co-cited.

#### **4. Main Subfields of Remote Sensing and Geographic Information Systems for Flash Floods**

In the past two decades, due to the continuous development of science and technology, there have been many subfields in the application of remote sensing and GIS to flash floods. This article introduces five main subfields of the application of remote sensing and GIS to flash floods.

#### *4.1. Flash Flood Forecasting*

Since a flash flood may occur suddenly and the time to reach the peak is short, the accuracy of any early warning of flash floods depends largely on the accuracy of precipitation monitoring and prediction [14,64].

Accurate and timely measurement of the temporal and spatial distribution of rainfall is the starting point for flash flood forecasting [64]. Due to the wide coverage of satellites, satellite data are regarded as an important data source for areas with sparse and uneven distributions of measurement stations. Satellite data have been widely used in meteorological research, and the ability to estimate rainfall directly affects the ability to observe flash flood time. Table 2 lists six representative studies on the evaluation of satellite precipitation products in recent years. These studies combined multiple precipitation products and evaluated them with multiple statistical indicators, abundant precipitation products, and relatively rich types of research areas covered.

**Table 2.** For evaluating satellite precipitation products.


The performance of precipitation products was evaluated for arid areas, mountain areas, and urban areas [17,79,81]. Satellite precipitation products can accurately detect and estimate extreme precipitation events. There are some uncertainties in the results obtained by using satellite precipitation products [82,83]. On the one hand, the algorithm can be optimized, and the inherent deviations in the precipitation calculation can be corrected to reduce the uncertainty. On the other hand, the integration of high-resolution and multisource precipitation analysis can be considered to compensate for the deficiency of a single precipitation product [85].
