The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19.

中文翻译：

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通过将GNSS-R信号与地形信息相结合的洪水淹没制图

旋风全球导航卫星系统（CYGNSS）任务以信噪比（SNR）点数据的形式收集接近全球的每小时，伪随机分布的全球导航卫星系统-反射法（GNSS-R）信号，由于它们在L波段的工作频率，因此对地表水的存在敏感。但是，由于这些点的伪随机性质，不可能以足够的高分辨率获得连续的洪水淹没图。通过考虑拓扑指标，例如最近的排水上方的高度（HAND）和最近的排水的坡度（SND），它们表明某个区域容易发生洪水的可能性，我们假设将静态地形信息与动态GNSS-R信号相结合可能会生成大规模的高分辨率洪水淹没图。使用可用的Sentinel-1A合成孔径雷达（SAR）数据对洪灾程度进行了洪灾制图并进行了验证，该数据用于2018年8月在喀拉拉邦和2017年8月在印度北部的洪灾。阈值化后获得的结果表明该模型表现出洪灾准确性对于较低的阈值，范围从60％到80％。我们在整个洪水期间的淹没图中观察到了明显的高估误差，导致阈值介于17-19之间的最佳临界成功指数为0.22。阈值化后获得的结果表明，对于较低的阈值，模型表现出的泛洪精度为60％至80％。我们在整个洪水期间的淹没图中观察到了明显的高估误差，导致阈值介于17-19之间的最佳临界成功指数为0.22。阈值化后获得的结果表明，对于较低的阈值，模型表现出的泛洪精度为60％至80％。我们在整个洪水期间的淹没图中观察到了明显的高估误差，导致阈值介于17-19之间的最佳临界成功指数为0.22。