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

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.

[1]  R. Manavalan,et al.  SAR image analysis techniques for flood area mapping - literature survey , 2017, Earth Science Informatics.

[2]  C. Kilsby,et al.  Multi‐temporal synthetic aperture radar flood mapping using change detection , 2018 .

[3]  Philip Marzahn,et al.  SAR-based detection of flooded vegetation – a review of characteristics and approaches , 2018 .

[4]  Liping Di,et al.  The state of the art of spaceborne remote sensing in flood management , 2016, Natural Hazards.

[5]  M. Marconcini,et al.  Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data , 2018 .

[6]  Emmanouil N. Anagnostou,et al.  Inundation Extent Mapping by Synthetic Aperture Radar: A Review , 2019, Remote. Sens..

[7]  Albert J. Kettner,et al.  Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment , 2018, Remote. Sens..

[8]  Riadh Abdelfattah,et al.  Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence , 2018, Remote. Sens..

[9]  Yu Li,et al.  Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion , 2019, Remote. Sens..

[10]  Nazzareno Pierdicca,et al.  Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case , 2019, Remote. Sens..

[11]  Emma M. Hill,et al.  Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew , 2019, Remote. Sens..

[12]  Ralf Ludwig,et al.  An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Yukio Endo,et al.  Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods , 2019, Remote. Sens..

[14]  Alberto Refice,et al.  SAR and InSAR for Flood Monitoring: Examples With COSMO-SkyMed Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Franz J. Meyer,et al.  Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh , 2019, Remote. Sens..

[16]  D. Leibovici,et al.  Rapid flood inundation mapping using social media, remote sensing and topographic data , 2017, Natural Hazards.

[17]  Simon Plank,et al.  Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery , 2017 .

[18]  Stefan Dech,et al.  Comparing four operational SAR-based water and flood detection approaches , 2015 .

[19]  Niko E. C. Verhoest,et al.  Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Fumio Yamazaki,et al.  Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery , 2018, Natural Hazards and Earth System Sciences.

[21]  Massimo Menenti,et al.  Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water , 2019, Remote. Sens..

[22]  Niko E. C. Verhoest,et al.  Accounting for image uncertainty in SAR-based flood mapping , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Dimitri Solomatine,et al.  A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level , 2015 .

[24]  Philip Marzahn,et al.  Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data , 2018, Remote. Sens..

[25]  Albert J. Kettner,et al.  Near-real-time non-obstructed flood inundation mapping using synthetic aperture radar , 2019, Remote Sensing of Environment.

[26]  Sandro Martinis,et al.  A fully automated TerraSAR-X based flood service , 2015 .

[27]  Simon Plank,et al.  Rapid Damage Assessment by Means of Multi-Temporal SAR - A Comprehensive Review and Outlook to Sentinel-1 , 2014, Remote. Sens..

[28]  Nazzareno Pierdicca,et al.  Analysis and Interpretation of the COSMO-SkyMed Observations of the 2011 Japan Tsunami , 2012, IEEE Geoscience and Remote Sensing Letters.

[29]  Sandro Martinis,et al.  Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany , 2015, Remote. Sens..

[30]  Manabu Watanabe,et al.  Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case of the 2015 Heavy Rainfall in Japan , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Fabiana Calò,et al.  Potential and Limitations of Open Satellite Data for Flood Mapping , 2018, Remote. Sens..

[32]  Raffaella Guida,et al.  A Novel Fully Automated Mapping of the Flood Extent on SAR Images Using a Supervised Classifier , 2019, Remote. Sens..

[33]  Nadhir Al-Ansari,et al.  Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier , 2020, Remote. Sens..

[34]  Paul D. Bates,et al.  A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Hiroyoshi Yamada,et al.  Review of the Comprehensive SAR Approach to Identify Scattering Mechanisms of Radar Backscatter from Vegetated Terrain , 2019, Electronics.

[36]  Giorgio Boni,et al.  Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Karem Chokmani,et al.  River flood mapping in urban areas combining Radarsat-2 data and flood return period data , 2017 .

[38]  Katarzyna Dabrowska-Zielinska,et al.  Monitoring Wetlands Ecosystems Using ALOS PALSAR (L-Band, HV) Supplemented by Optical Data: A Case Study of Biebrza Wetlands in Northeast Poland , 2014, Remote. Sens..

[39]  Youngjoo Kwak,et al.  Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data , 2017, ISPRS Int. J. Geo Inf..