# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 4 Results and Discussion
Figure 5: (a) Flood inundation derived from Sentinel-1 synthetic aperture radar (SAR) imagery; (b) model (M) output for threshold 15 and Sentinel (S) flood inundation maps (water—1, and no water—0) overlaid on each other over Kerala on 23 August 2018. Figure 6: Mapping critical success index (CSI) and flood accuracy (FA) of proposed model output, compared with the Sentinel-derived flood inundation map for the Kerala flood event of August 2018. The model results were found to be most sensitive around values of 15-19, after which FA and CSI decreased considerably. For lower values of model threshold, high values of FA were observed, but the corresponding value of CSI indicates the presence of model overestimation. In the case of the North India flood event of August 2017, several Sentinel-1 SAR scenes were collected across different days (24-29 August) of the flood period, subjected to thresholding, and mosaicked to obtain a rough extent of inundation across such a large study region (Figure 7a). Figure 7: **(a)** Flood inundation derived from Sentinel-1 SAR imagery; and **(b)** model (M) output for threshold 15 and Sentinel (S) flood inundation maps (water—1, and no water—0) overlaid on each other over North India for August 2017. Similar to the case of the flood event in Kerala, the model output was subjected to thresholding for different values. The overlapped inundation map obtained for a sample threshold value of 15 with the reference inundation map from Sentinel-1 SAR shows that there exists a large amount of overestimation error by the model (Figure 7b). This overestimation by the model was found to be mainly caused by:  
1. Coarse spatial resolution of the CYGNSS SNR points, which are spaced widely apart, whose signal strength also depends spatially on the first Fresnel zone of the coherently reflecting surface. 2. Poor vertical accuracy and coarse spatial resolution of the ASTER DEM in resolving the floodplain areas, which caused the HAND flood terrain model being unable to distinguish between the river channel and the delta regions. 3. Sentinel-1 SAR imagery suffering from larger temporal latency and therefore failing to capture the maximum inundation, which will be complemented by the daily available CYGNSS dataset. 4. Presence of many irrigated paddy fields in the fertile area of the Ganga and Brahmaputra delta region, as well as the presence of dense vegetation in the case of Kerala, can result in the proposed model identifying those high-moisture regions as being inundated due to flood event. Also, Figure 8 underlines that the model result was sensitive around 17-21, much as the earlier CSI plot obtained for the Kerala flood event. At higher threshold model values (>20), substantial underestimation errors in inundation mapping exist, which shows that the proposed model was highly sensitive around model values of 17-19 when considering both cases. However, the model threshold value cannot definitively be fixed for a given flood event to obtain the flooding extent, because of the varied land-use/land-cover usage (including interference due to non-flooding attributes in the case of inundated paddy fields dominating the North India study region) and flooding behaviors associated with the study regions. Furthermore, the model structure was modified by including an exponential parameter, \(\beta\), to the static (HAND and SND) part of the term which was varied from 0.1 to 10, resulting in similar CSI values for a given model threshold value for both the cases. This insensitivity to model structure variation is due to the over-/underestimation of the inundation extent as obtained by the HAND/SND threshold, as this is more sensitive to the channel initiation value (contributing area) [27] than assigning more/less weight to the HAND/SND values in itself.