# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 1 Introduction  
Floods are among the worst natural disasters, affecting millions of people across different parts of the world. The magnitude of these flood events is projected to increase [1] because of exacerbated climate change and rapid urbanization, thus making flood control and management an important agenda on the Sendai Framework for Disaster Risk Reduction [2]. To alleviate the impact of floods on the surrounding environment, and to enhance decision making for emergency response and recovery, a comprehensive global flood risk assessment must be carried out, quantifying flood hazard, exposure, and vulnerability. Flood hazard [3] is defined as the occurrence probability of a flood event within a region. Hence, in order to quantify the probability of flooding of a region, we need accurate flood area maps with high temporal and spatial resolutions that are associated with a given rainfall and discharge volume. Earlier methods of producing spatial flood maps were based on manual observation of flood extents, which are not only laborious but also affect the safety of people undertaking observations in flooding conditions. Satellite-based remote sensing observation is currently used for flood inundationmapping, as it provides a synoptic view of a large area [4; 5; 6; 7; 8]. However, the presence of clouds acts as a hindrance to the use of optical remote sensing images. Since most flood events are characterized by heavy rainfall, this limits the detailed mapping of the inundation below clouds. Synthetic aperture radar (SAR)-based flood mapping techniques have the advantage of an all-weather, all-time capability that has found a lot of applications during floods [9; 10; 11]. However, most of the open data source SAR satellites have poor revisit times (in the order of ~10-14 days), which are larger than the typical flood event durations that last around 3-7 days. Hence, unless in a constellation, space-borne SAR missions generally fail to capture the spatial evolution of the flooding event, even though they may capture the flooding for a given day more accurately when compared to optical remote sensing methods. In the case of rare flood events, where the magnitude of the flood peak is such that the swath width of the satellite in one day cannot fully cover the flooded region, SAR-derived flood maps may not be able to capture the entire extent of flooding, in particular for extensive flood events such as in Chennai during December 2015 [12], and the nearby state of Kerala during August 2018 [13], both located in western India. In both instances, the catchments received their highest rainfall in a century [14; 15]. Both flood events were characterized by heavy rainfall in the catchment area, followed by dams in the basin area being filled to their maximum capacity, and dam authorities eventually having to release enormous quantities of water to prevent dam failure [16]. Subsequently, large areas were inundated across the floodplain region, resulting in the deaths of over 400 people and 100,000 heads of livestock, while displacing millions from their homes and damaging crops spread across an area of 4000 km\({}^{2}\)[17; 15]. The extent was so large that the available SAR images during the flooding period did not cover the flooded region entirely. It must be noted that, in the case of the Chennai floods, much of the dam operations were performed during the night, catching the people unaware of the rising flood levels, thereby aggravating the problem of rescue and recovery [18]. Also, there exist significant challenges in identifying flooding under vegetation and for urban areas from SAR imagery, which are currently overcome by considering other ancillary information, including land cover maps and optical imagery [10].