## 4 Method and Discussion
### Evaluation and Mapping  
As can be seen in Figure 4B, the flood happened in the south and middle part of the Sistan and Baluchestan province in Iran. Figure 10 shows the flooded regions which were detected by CYGNSS observations overlaid on the MODIS image (Figure 4B) for verification. The figure contains three regions with significant SNR anomalies. The regions are labeled A, B, and C.  
Region A in Figure 10 belongs to the Hamun-Jaz Murian depression in the southeast of Iran, placed between the Kerman province and Sistan and Baluchestan province. The shape of the depression or basin is oblong and enclosed by the mountains. There is a seasonal lake, Hamun, in the middle of the basin, which has been dry through the recent dry years. Although the Halil and Bampur rivers are the main sources of feeding for the basin, neither of both bring significant water to the basin to fill this lake, because the water is used for agricultural purposes on the way [49; 50]. Moreover, the recent flood in January 2020 was unique in terms of flood volume over the last decade. The previous flood in this region happened in June 2007. Figure 11 demonstrates the capability of CYGNSS measurements in the detection and mapping of the flood over this depression. To calculate the flooded areas using corrected SNR, a threshold was used to distinguish inundated from noninundated areas. A simple threshold method has been used in previous studies with monostatic and bistatic radars [11; 18]. As is seen in Figures 10-13, observations with the values greater than 11 dB corresponded to the flooded areas. This threshold was used for the detection of inundation in this study. This value could be different in other regions. The roughness and vegetation could weaken the signals and change the threshold. The threshold used by [11] was 12 dB for the medium-vegetation density and typical roughness. As can be distinguished from flooded areas in Figures 11 and 12, the values of corrected SNR more than 11 dB have a high correlation with the satellite image in the inundated region. However, minor discrepancies could be related to georeferencing or interpolation errors. The overall evaluation of the results using the three days of CYGNSS data reports an acceptable performance for flood detection. Figure 9: The outcome of the interpolation process for the corrected SNR (\(SNR_{c~{}dB}\)) over the period of three days from 13 January to 15 January 2020. (**A**) Representation of the CYGNSS measurements along the satellite tracks, (**B**) the interpolated data at 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) grid points using the natural neighbor interpolation method. Governments could use flood maps to establish the risk regions, safe evacuation options, and update the reaction plan. In the absence of promising and accurate flood maps, the development processes in or nearby the risk area are affected. The community lacks a tool to guide development to be more secure and to reduce future risks. Figure 11: The georeferenced optical satellite imagery of the flood over the Hamun-Jaz Murian basin (region A in Figure 10) from MODIS (13 January 2020) overlaid by the corrected SNR derived from CYGNSS observations (13 January to 15 January 2020). Figure 10: The georeferenced optical satellite imagery of the flood from MODIS (13 January 2020) overlaid by the corrected signal to noise ratio derived from CYGNSS observations (13 January to 15 January 2020). The regions labeled A, B, and C show significant SNR anomalies. We proceeded to map the detected inundation area. Google Maps was used here as an infrastructure which provides information about roads, cities, villages, etc. The derived data which shows flooded regions (data over 11 dB) was mapped on Google Maps. Figure 14 illustrates the three major regions of flood in Sistan and Baluchestan. Due to the flood in region A, corresponding to the Hamun-Jaz-Murian basin, the cities close to the basin, i.e., IranShahr, Eslam Abad, and Golmorti, and the roads between them, were affected.