Estimation of Flood Inundation and Depth During Hurricane Florence Using Sentinel-1 and UAVSAR Data

Abstract

We studied the temporal and spatial changes in flood water elevation and variation in the surface extent due to flooding resulting from Hurricane Florence (September 2018) using the L-band observation from an unmanned aerial vehicle synthetic aperture radar (UAVSAR) and C-band synthetic aperture radar (SAR) sensors on Sentinel-1. The novelty of this study lies in the estimation of the changes in the flood depth during the hurricane and investigating the best method. Overall, flood depths from SAR were observed to be well-correlated with the spatially distributed ground-based observations ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} = 0.79$ </tex-math></inline-formula> –0.96). The corresponding change in water level ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\partial \text{h}/\partial \text{t}$ </tex-math></inline-formula> ) also compared well between the remote sensing approach and the ground observations ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} = 0.90$ </tex-math></inline-formula> ). This study highlights the potential use of SAR remote sensing for inundated landscapes (and locations with scarce ground observations), and it emphasizes the need for more frequent SAR observations during flood inundation to provide spatially distributed and high temporal repeat observations of inundation to characterize flood dynamics.