# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 5 Conclusions  
GNSS-R Signals of Opportunity (SOOP) are sensitive to the presence of surface water. The study proceeded, considering these signals to be highly temporal (sub-daily); however, because of the poor spatial resolution (order of kilometers) available for a given region from CYGNSS [39], SNR data points were accumulated and interpolated spatially, employing a moving window approach before and during a flood event. The maximum observed SNR value during the flooding period provides an opportunity to estimate the maximum flood inundation extent while integrating those with topographical information. Thus, this paper explored the potential of combining the coarse resolution, but temporally dynamic, GNSS-R signals with the static, but spatially continuous, HAND-SND-based flood terrain index for high-resolution global flood inundation mapping. CYGNSS SNR daily data was extracted for two flood events, one over the southern Indian state of Kerala during August 2018, and for an extensive event during August 2017 across Bangladesh and parts of North and Northeast India. HAND and SND values were calculated for the respective study regions and were integrated with CYGNSS SNR data using the proposed approach. The model was evaluated with flood inundation maps derived from Sentinel-1 SAR imagery available during the flood duration. It was found that the model-derived inundation extent was sensitive to values around 17-19 on thresholding with a maximum CSI of 0.22, although there was high flooding accuracy (FA) in mapping 60-80% of flood extent for lower model threshold values. The significant over-/underestimation is mainly attributed to the spatially-coarse pseudo-randomly distributed SNR data, and to the limited availability of the Sentinel-1 SAR imagery. This method thus gives an advantage over existing SAR satellite-derived flood inundation methods, because (a) SAR-derived flood maps provide only an incomplete snapshot, not covering the entire extent of flooding, whereas in the case of CYGNSS, a near-global daily coverage is available; and (b) because of low revisit frequency in the case of SAR satellites, they are not able to provide information about the spatial dynamics of the flood event. Thus, there exists no single standard dataset which can satisfy all the conditions that are necessary for the temporal evolution of flood inundation extent to be captured with reasonable accuracy. With a larger amount of GNSS-R data available in the future, this approach provides an opportunity that would greatly benefit peak flood extent estimation, and also help in mapping floods at the sub-daily temporal resolution, which can provide useful information when a flood intensifies and recedes.  
The following are available online at [[http://www.mdpi.com/2072-4292/12/18/3026/s1](http://www.mdpi.com/2072-4292/12/18/3026/s1)]([http://www.mdpi.com/2072-4292/12/18/3026/s1](http://www.mdpi.com/2072-4292/12/18/3026/s1)), Table S1: Details of the CYGNSS SNR dataset used along with observed flood extent considered in this study; Table S2: Details of the Sentinel--1 SAR imagery used in this study.  
Conceptualization: S.L.K.U. and B.B.; methodology: S.L.K.U., B.B., and C.R.; validation: S.L.K.U. and C.R.; formal analysis: S.L.K.U., B.B., and C.R.; resources: S.L.K.U.; data curation: S.L.K.U.; writing--original draft preparation: S.L.K.U.; writing--review and editing: B.B. and C.R.; visualization: S.L.K.U., B.B., and C.R.; supervision: B.B. and C.R. All authors have read and agreed to the published version of the manuscript.  
This research received funding from CSIRO Data61, Australia.  
The first author acknowledges the support from IITB-Monash Research Academy through the joint Ph.D. program between the Indian Institute of Technology Bombay, India, and Monash University, Australia.  
The authors declare no conflict of interest.