# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 1 Introduction
However, current and future SAR missions, including NISAR, TerraSAR/TanDEM-L, CSK-2, the Radarsat constellation, and open data access policies between organizations in charge of their data cataloguing, can lead to sufficiently extensive temporal SAR inventory of the Earth's surface that can, in turn, result in comprehensive remote observations of flood extents across the world. However, until those missions are fully operational, there still exist significant challenges in reliably estimating the spatial extent of flooding. Global Navigation Satellite System Reflectometry (GNSS-R) is the technique of measuring the reflected signals from navigation satellites, the most popular being the Global Positioning System (GPS), which provides daily, near-global point information over land, ocean, and atmosphere [19]. The Cyclone Global Navigation Satellite System (CYGNSS) mission was originally planned to improve the understanding of cyclonic behavior with sub-daily wind speed measurements. Recently, this data has found land surface applications in the form of large-scale flood inundation mapping [20; 21] through change detection approaches. The signals operating in the L-band penetrate clouds, and are less affected by signal attenuation through heavy rainfall and vegetation than observations in other frequencies, and thus provide an all-time and -weather observational capability. The inland areas, when submerged in water, have a distinctive increase in signal strength when compared to non-water surfaces. Water, being a smooth scatterer, reflects most of the incoming GPS signals, and hence the signals received by the 8-satellite CYGNSS constellation from a water-covered surface have enhanced signal strength. Thus, GNSS-R acts, in this case, as a dynamic indicator of the presence of water on a near-global daily scale. However, the reflected GPS data points are pseudo-randomly distributed as a function of the satellite positions, and hence the same location may not be mapped periodically, effectively resulting in a coarse daily spatial footprint for CYGNSS over land surfaces. Hence, it is hypothesized that upon integrating fine resolution static topographic information with highly frequent signal-to-noise ratio (SNR) data, it will be feasible to obtain actionable flood inundation maps that provide useful information regarding how susceptible to inundation a given region will be for a given flood event. Height above nearest drainage (HAND) is a topography-based conceptual flood terrain index that quantifies the floodplains along the river reach into pixels, which are identified by their relative height to the nearest drainage point along the flow direction from the corresponding pixels [22; 23; 24]. However, it is a static flood terrain index that does not provide any information about the propagation speed of the flood wave, or if an area will be inundated or not. The index also requires a flood height cut-off, which acts as a threshold for the floodplain area, which in turn may then be used to derive the flood inundation extent. Studies have shown that there exists a large amount of overestimation of the floodplain area, depending on the threshold value chosen and the quality of the digital elevation model (DEM) used for estimating HAND values [25; 26; 27; 28]. In addition to the HAND index, which provides information on the floodplain, further information is therefore needed to characterize the flooding potential of a channel pixel, which can be obtained by the slope of nearest drainage (SND), which is a function of the height of water in it. Thus, HAND and SND together act as static indicators for deriving flood extent, while CYGNSS provides dynamic information. In this work, complimentary dynamic CYGNSS and static HAND-SND datasets are combined to map the extent of floodplain areas arising in the case of riverine floods. The proposed model was analyzed and compared against Sentinel-1A SAR-derived flood maps [7]. The error associated with the corresponding threshold values of the proposed method was determined.