# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 4 Results and Discussion  
After processing the ASTER DEM, the HAND flood terrain model for Kerala was obtained, as shown in Figure 3a. The figure shows that Kerala is enveloped by the mountainous Western Ghats to the East, which is associated with high HAND and SND values (Figure 3b), and the relatively flat flood plains along the east coast on the left. The CYGNSS Level-1 BRCS SNR daily data was extracted over the southern Indian state of Kerala for the August 2018 flood event, accumulated across the 30-day window period before and after the flood event and spatially interpolated, yielding the maximum SNR values, as shown in Figure 3c. The coastal areas are clearly associated with higher SNR values than the mountainous Western Ghats. These values are indicative of surface water present in the pixel and coincide with ground observations of flood points reported at the time [34]. However, the SNRdata alone cannot accurately map the inundation at the river channel scale, because of the mixed pixel response from the water surface along the coastlines. It can only provide overall information on which places could have been inundated across a large spatial extent of the study region. Hence, the proposed merged model output from CYGNSS, HAND, and SND data yields possible inundation, as shown in Figure 3d. The model output indicates areas that have higher values in the low-lying parts of the central and southern coastal areas, which witnessed extensive flooding [34]. In the case of the August 2017 flood event across North India and its neighboring regions, most of the terrain, except the mountainous Arunachal Pradesh, is flat, and associated with low HAND (Figure 4a) and SND (Figure 4b) values across the floodplains of the Ganga and Brahmaputra rivers, making it prone to extensive flooding. The processed CYGNSS data indicates large areas with significant SNR values (\(<\)15) across the study region, as shown in Figure 4c. The combined output also shows a significant increase in model value across large portions of the floodplain areas, given in Figure 4d. It must be noted that HAND, and SND as its extension, were chosen based on a better conceptual representation of hydrologic similarity of floodplain regions than other topographic indices [35]. Figure 3: Case I (**a**) HAND flood terrain model for Kerala; (**b**) SND obtained for Kerala; (**c**) maximum SNR obtained from the Cyclone Global Navigation Satellite System (CYGNSS) for August 2018; (**d**) proposed model output for Kerala during floods of August 2018. Figure 4: Case II (a) HAND flood terrain model for North India; (b) SND obtained for North India; (c) maximum SNR obtained from CYGNSS for August 2017; (d) model output for North India during floods of August 2017. In the case of the Kerala flood event, Sentinel-1A could capture the flood inundation only on August 21, 2018, and hence imagery available (three scenes) over parts of Kerala state was used for deriving inundation extent by thresholding the pre-processed SAR image using [PERSON]'s method of binarization. The inundation extent was derived at 30 m spatial resolution (shown in Figure 5a) to make it comparable with the proposed model result. The reference inundation map exhibits a similar inundation pattern, as shown by the model output inundation extent. The model output was then subjected to different threshold values, to compare how the extent of inundation matched with the validation inundation map obtained from SAR imagery. Figure 5b shows the maximum extent of inundation obtained after considering a model output threshold value of 15 overlaid with the validation inundation map with the percentage overlap of flooded and non-flooded areas, as well as the over- and underestimation by the proposed model indicated. Figure 6 shows the variation of the CSI calculated (from (7)) between both the inundation maps with the threshold value.