Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches

Landuyt, Lisa; Wesemael, Alexandra Van; Schumann, Guy; Hostache, Renaud; Verhoest, Niko; Coillie, Frieke M. B. Van

doi:10.1109/tgrs.2018.2860054

Cited by 94 publications

(65 citation statements)

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“…In particular, spaceborne Synthetic Aperture Radar (SAR) systems are suitable tools for flood mapping thanks to their daytime and nighttime and almost all-weather imaging capability, in addition to their sensitivity to surface roughness and to soil moisture changes [5]. In the field of remote sensing imagery, many image processing approaches have been implemented using SAR data to delineate the flooded areas [6][7][8]. In this context, in the case of bare soil, the main premise is the existence of specular reflection from the smooth water bodies which produces a low radar backscattering signal [9].…”

Section: Introductionmentioning

confidence: 99%

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

et al. 2018

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In this paper, we assess the flood mapping capabilities of the X-band Synthetic Aperture Radar (SAR) imagery acquired by the bistatic pair TanDEM-X/TerraSAR-X (TDX/TSX). The main objective is to investigate the added value of the bistatic TDX/TSX Interferometric Synthetic Aperture Radar (InSAR) coherence in addition to the SAR backscatter in the context of inundation mapping. As a classifier, we consider a Random Forest (RF) classification scheme using TDX/TSX SAR intensities and their bistatic InSAR coherence to extract the flood extent map. To evaluate the classification results and as no "ground truth" was available at the SAR data acquisition time, we set up a LISFLOOD-FP hydraulic model for simulating the temporal evolution of the flood water. The flood map simulated by the model shows good performances with an Overall Accuracy (OA) of 97.92% and a Critical Success Index (CSI) of 94.01%. The SAR-derived flood map is then compared to the LISFLOOD-FP extent map simulated at the SAR data acquisition time. As a test case, we consider the flooding event of the Richelieu River that occurred in the Montérégie region of Quebec (Canada) from April to June 2011. Experimental results highlight the potential of the bistatic InSAR coherence for more accurate flood mapping in a complex landscape with urban and vegetation areas. The classification results of the SAR-derived flood map with respect to the LISFLOOD-FP flood map reach an OA of 78.65% and a Precision of 82.08% when integrating the bistatic InSAR coherence. These classification OA and Precision values are 69.63% and 64.52%, respectively, using only the TDX/TSX SAR intensity.

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Section: Introductionmentioning

confidence: 99%

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

et al. 2018

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“…Major SAR sources have been closely examined as part of a coherence analysis, which found that coherence analysis is appropriate for inundation detection in urbanized areas. The latest proposals have confirmed these findings through comparison among numerous statistical and/or machine learning methods, which could achieve a 90% overall accuracy [33,34]. Nowadays, the development of fully automatic systems has become an achievable goal [35,36].…”

Section: Introductionmentioning

confidence: 83%

Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019

Natsuaki

Nagai

2020

Remote Sensing

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Flood detection using a spaceborne synthetic aperture radar (SAR) has become a powerful tool for organizing disaster responses. The detection accuracy is increased by accumulating pre-event observations, whereas applying multiple observation modes results in an inadequate number of observations with the same mode from the same orbit. Recent flood detection studies take advantage of the large number of pre-event observations taken from an identical orbit and observation mode. On the other hand, those studies do not take account of the use of multiple orbits and modes. In this study, we examined how the analysis results suffered when pre-event observations were only taken from a different orbit or mode to that of the post-event observation. Experimental results showed that inundation areas were overlooked under such non-ideal conditions. On the other hand, the detection accuracy could be recovered by combining analysis results from possible alternate datasets and became compatible with ideal cases.

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“…thresholding on backscatter low levels [44], [45], which are anyway out of the scope of the present work.…”

Section: Methodsmentioning

confidence: 99%

Repeat-pass SAR interferometry for land cover classification: A methodology using Sentinel-1 Short-Time-Series

Sica

Pulella

Nannini

et al. 2019

Remote Sensing of Environment

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Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches

Cited by 94 publications

References 50 publications

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019

Repeat-pass SAR interferometry for land cover classification: A methodology using Sentinel-1 Short-Time-Series

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