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

IF 4 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-08-26 DOI:10.1002/essoar.10507902.1

S. Kundu, V. Lakshmi, R. Torres

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引用次数: 1

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 (

$R^{2} = 0.79$

–0.96). The corresponding change in water level (

$\partial \text{h}/\partial \text{t}$

) also compared well between the remote sensing approach and the ground observations (

$R^{2} = 0.90$

). 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.

查看原文本刊更多论文

利用Sentinel-1和UAVSAR数据估算飓风佛罗伦萨期间的洪水淹没和深度

我们使用无人驾驶飞行器合成孔径雷达（UAVSAR）和Sentinel-1上的C波段合成孔径雷达传感器的L波段观测，研究了飓风佛罗伦萨（2018年9月）引发的洪水导致的洪水水位的时间和空间变化以及地表范围的变化。这项研究的新颖之处在于估计飓风期间洪水深度的变化，并研究最佳方法。总的来说，SAR观测到的洪水深度与空间分布的地面观测结果有很好的相关性（$R^｛2｝=0.79$–0.96）。相应的水位变化（$\partial\text｛h｝/\partial\text{t｝$）也与遥感方法和地面观测结果进行了很好的比较（$R^{2｝=0.90$）。这项研究强调了SAR遥感在被淹没景观（以及地面观测稀少的地点）中的潜在用途，并强调了在洪水淹没期间需要更频繁的SAR观测，以提供洪水的空间分布和高时间重复观测，从而表征洪水动态。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理

CiteScore

7.60

自引率

12.50%

发文量

1113

审稿时长

3.4 months

期刊介绍： IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.