Abstract
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.
Highlights
Floods are among the worst natural disasters, affecting millions of people across different parts of the world
This paper explored the potential of combining the coarse resolution, but temporally dynamic, GNSS–R signals with the static, but spatially continuous, height above nearest drainage (HAND)-slope of nearest drainage (SND)-based flood terrain index for high-resolution global flood inundation mapping
Cyclone Global Navigation Satellite System (CYGNSS) signal-to-noise ratio (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
Summary
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, making flood control and management an important agenda on the Sendai Framework for Disaster Risk Reduction [2]. 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. 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
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