24.2.2.3.1 SAR and Radar for Flood Analysis, Flood Mapping, Flood Monitoring

Chapter Contents (Back)
Flood Analysis. SAR. Radar.

Horritt, M.S., Mason, D.C., Cobby, D.M., Davenport, I.J., Bates, P.D.,
Waterline mapping in flooded vegetation from airborne SAR imagery,
RSE(85), No. 3, 30 May 2003, pp. 271-281.
Elsevier DOI 0309
BibRef

Mason, D.C., Horritt, M.S., Dall'Amico, J.T., Scott, T.R., Bates, P.D.,
Improving River Flood Extent Delineation From Synthetic Aperture Radar Using Airborne Laser Altimetry,
GeoRS(45), No. 12, December 2007, pp. 3932-3943.
IEEE DOI 0711
BibRef

Schumann, G., di Baldassarre, G., Bates, P.D.,
The Utility of Spaceborne Radar to Render Flood Inundation Maps Based on Multialgorithm Ensembles,
GeoRS(47), No. 8, August 2009, pp. 2801-2807.
IEEE DOI 0907
BibRef

Mason, D.C., Davenport, I.J., Neal, J.C., Schumann, G.J.P., Bates, P.D.,
Near Real-Time Flood Detection in Urban and Rural Areas Using High-Resolution Synthetic Aperture Radar Images,
GeoRS(50), No. 8, August 2012, pp. 3041-3052.
IEEE DOI 1208
BibRef

Cruz, V.H.[Virginia Herrera], Müller, M.[Marc], Weise, C.[Christian],
Flood Extent Mapping Based on TerraSAR-X Data,
PFG(2010), No. 6, 2010, pp. 475-488.
WWW Link. 1211
BibRef

Giustarini, L., Hostache, R., Matgen, P., Schumann, G.J.P., Bates, P.D., Mason, D.C.,
A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X,
GeoRS(51), No. 4, April 2013, pp. 2417-2430.
IEEE DOI 1304
BibRef

Kuenzer, C., Guo, H., Huth, J., Leinenkugel, P., Li, X., Dech, S.,
Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses,
RS(5), No. 2, February 2013, pp. 687-715.
DOI Link 1303
BibRef

Martinis, S.[Sandro], Twele, A.[André], Strobl, C.[Christian], Kersten, J.[Jens], Stein, E.[Enrico],
A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains,
RS(5), No. 11, 2013, pp. 5598-5619.
DOI Link 1312
BibRef

Kuenzer, C.[Claudia], Guo, H.D.[Hua-Dong], Schlegel, I.[Inga], Tuan, V.Q.[Vo Quoc], Li, X.[Xinwu], Dech, S.[Stefan],
Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province,
RS(5), No. 10, 2013, pp. 5122-5142.
DOI Link 1311
BibRef

Iervolino, P., Guida, R., Iodice, A., Riccio, D.,
Flooding Water Depth Estimation With High-Resolution SAR,
GeoRS(53), No. 5, May 2015, pp. 2295-2307.
IEEE DOI 1502
floods BibRef

Martinis, S.[Sandro], Kersten, J.[Jens], Twele, A.[André],
A fully automated TerraSAR-X based flood service,
PandRS(104), No. 1, 2015, pp. 203-212.
Elsevier DOI 1505
SAR BibRef

Chapman, B.[Bruce], McDonald, K.[Kyle], Shimada, M.[Masanobu], Rosenqvist, A.[Ake], Schroeder, R.[Ronny], Hess, L.[Laura],
Mapping Regional Inundation with Spaceborne L-Band SAR,
RS(7), No. 5, 2015, pp. 5440-5470.
DOI Link 1506
BibRef

Martinis, S.[Sandro], Rieke, C.[Christoph],
Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany,
RS(7), No. 6, 2015, pp. 7732.
DOI Link 1507
BibRef

Chung, H.W.[Hsiao-Wei], Liu, C.C.[Cheng-Chien], Cheng, I.F.[I-Fan], Lee, Y.R.[Yun-Ruei], Shieh, M.C.[Ming-Chang],
Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation,
RS(7), No. 9, 2015, pp. 11954.
DOI Link 1511
BibRef

Pulvirenti, L., Chini, M., Pierdicca, N., Boni, G.,
Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence,
GeoRS(54), No. 3, March 2016, pp. 1532-1544.
IEEE DOI 1603
Backscatter BibRef

d'Addabbo, A., Refice, A., Pasquariello, G., Lovergine, F.P., Capolongo, D., Manfreda, S.,
A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data,
GeoRS(54), No. 6, June 2016, pp. 3612-3625.
IEEE DOI 1606
belief networks BibRef

Pradhan, B., Tehrany, M.S., Jebur, M.N.,
A New Semiautomated Detection Mapping of Flood Extent From TerraSAR-X Satellite Image Using Rule-Based Classification and Taguchi Optimization Techniques,
GeoRS(54), No. 7, July 2016, pp. 4331-4342.
IEEE DOI 1606
Earth BibRef

Giustarini, L., Hostache, R., Kavetski, D., Chini, M., Corato, G., Schlaffer, S., Matgen, P.,
Probabilistic Flood Mapping Using Synthetic Aperture Radar Data,
GeoRS(54), No. 12, December 2016, pp. 6958-6969.
IEEE DOI 1612
floods BibRef

Landuyt, L.[Lisa], van Wesemael, A.[Alexandra], Schumann, G.J.P.[Guy J.P.], Hostache, R.[Renaud], Verhoest, N.E.C.[Niko E. C.], van Coillie, F.M.B.[Frieke M. B.],
Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches,
GeoRS(57), No. 2, February 2019, pp. 722-739.
IEEE DOI 1901
Synthetic aperture radar, Histograms, Standards, Floods, Active contours, Entropy, Urban areas, Active contours, thresholding BibRef

Vanderhoof, M.K.[Melanie K.], Distler, H.E.[Hayley E.], Mendiola, D.T.G.[Di_Ana Teresa G.], Lang, M.[Megan],
Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Nakmuenwai, P.[Pisut], Yamazaki, F.[Fumio], Liu, W.[Wen],
Automated Extraction of Inundated Areas from Multi-Temporal Dual-Polarization RADARSAT-2 Images of the 2011 Central Thailand Flood,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef
Earlier: A2, A3, Only:
Extraction Of Flooded Areas Due The 2015 Kanto-tohoku Heavy Rainfall In Japan Using Palsar-2 Images,
ISPRS16(B8: 179-183).
DOI Link 1610
BibRef

Chini, M., Hostache, R., Giustarini, L., Matgen, P.,
A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case,
GeoRS(55), No. 12, December 2017, pp. 6975-6988.
IEEE DOI 1712
Backscatter, Distribution functions, Estimation, Histograms, Spatial resolution, Speckle, Synthetic aperture radar, synthetic aperture radar (SAR) BibRef

Tong, X.H.[Xiao-Hua], Luo, X.[Xin], Liu, S.G.[Shu-Guang], Xie, H.[Huan], Chao, W.[Wei], Liu, S.[Shuang], Liu, S.J.[Shi-Jie], Makhinov, A.N., Makhinova, A.F., Jiang, Y.Y.[Yu-Ying],
An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery,
PandRS(136), 2018, pp. 144-153.
Elsevier DOI 1802
SAR, Water extraction, Support vector machine, Active contour, Inundation analysis BibRef

Boergens, E.[Eva], Nielsen, K.[Karina], Andersen, O.B.[Ole Baltazar], Dettmering, D.[Denise], Seitz, F.[Florian],
River Levels Derived with CryoSat-2 SAR Data Classification: A Case Study in the Mekong River Basin,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Sghaier, M.O.[Moslem Ouled], Hammami, I.[Imen], Foucher, S.[Samuel], Lepage, R.[Richard],
Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Amitrano, D., di Martino, G., Iodice, A., Riccio, D., Ruello, G.,
Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images,
GeoRS(56), No. 6, June 2018, pp. 3290-3299.
IEEE DOI 1806
Feeds, Floods, Fuzzy systems, Indexes, Spatial resolution, Synthetic aperture radar, Classification, co-occurrence texture, synthetic aperture radar (SAR) BibRef

Amitrano, D., di Martino, G., Iodice, A., Mitidieri, F., Papa, M.N., Riccio, D., Ruello, G.,
Mapping small reservoirs in semi-arid regions using multitemporal SAR: Methods and applications,
MultiTemp17(1-4)
IEEE DOI 1712
hydrological techniques, remote sensing, reservoirs, synthetic aperture radar, area measurements, innovative method, BibRef

Chaabani, C.[Chayma], Chini, M.[Marco], Abdelfattah, R.[Riadh], Hostache, R.[Renaud], Chokmani, K.[Karem],
Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Chini, M.[Marco], Pelich, R.[Ramona], Pulvirenti, L.[Luca], Pierdicca, N.[Nazzareno], Hostache, R.[Renaud], Matgen, P.[Patrick],
Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Benoudjit, A.[Abdelhakim], Guida, R.[Raffaella],
A Novel Fully Automated Mapping of the Flood Extent on SAR Images Using a Supervised Classifier,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Li, Y.[Yu], Martinis, S.[Sandro], Wieland, M.[Marc],
Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence,
PandRS(152), 2019, pp. 178-191.
Elsevier DOI 1905
Urban flooding, Multi-temporal SAR, Interferometric coherence, Active learning, Self-learning, Convolution neural network BibRef

Uddin, K.[Kabir], Matin, M.A.[Mir A.], Meyer, F.J.[Franz J.],
Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Lin, Y.N.[Yunung Nina], Yun, S.H.[Sang-Ho], Bhardwaj, A.[Alok], Hill, E.M.[Emma M.],
Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Mahdavi, S.[Sahel], Salehi, B.[Bahram], Huang, W.M.[Wei-Min], Amani, M.[Meisam], Brisco, B.[Brian],
A PolSAR Change Detection Index Based on Neighborhood Information for Flood Mapping,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Li, Y.[Yu], Martinis, S.[Sandro], Wieland, M.[Marc], Schlaffer, S.[Stefan], Natsuaki, R.[Ryo],
Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Shen, X.[Xinyi], Wang, D.C.[Da-Cheng], Mao, K.[Kebiao], Anagnostou, E.[Emmanouil], Hong, Y.[Yang],
Inundation Extent Mapping by Synthetic Aperture Radar: A Review,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Aristizabal, F.[Fernando], Judge, J.[Jasmeet], Monsivais-Huertero, A.[Alejandro],
High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Nemni, E.[Edoardo], Bullock, J.[Joseph], Belabbes, S.[Samir], Bromley, L.[Lars],
Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Ma, Y.[Yu], Chen, H.[Haonan], Ni, G.[Guangheng], Chandrasekar, V., Gou, Y.[Yabin], Zhang, W.J.[Wen-Juan],
Microphysical and Polarimetric Radar Signatures of an Epic Flood Event in Southern China,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Liang, J.Y.[Jia-Yong], Liu, D.S.[De-Sheng],
A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery,
PandRS(159), 2020, pp. 53-62.
Elsevier DOI 1912
Flood mapping, Water delineation, Sentinel-1, SAR, Backscatter distribution, Thresholding BibRef

Rosenqvist, J.[Jessica], Rosenqvist, A.[Ake], Jensen, K.[Katherine], McDonald, K.[Kyle],
Mapping of Maximum and Minimum Inundation Extents in the Amazon Basin 2014-2017 with ALOS-2 PALSAR-2 ScanSAR Time-Series Data,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Huang, M.M.[Min-Min], Jin, S.G.[Shuang-Gen],
Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Singha, M.[Mrinal], Dong, J.W.[Jin-Wei], Sarmah, S.[Sangeeta], You, N.[Nanshan], Zhou, Y.[Yan], Zhang, G.[Geli], Doughty, R.[Russell], Xiao, X.M.[Xiang-Ming],
Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine,
PandRS(166), 2020, pp. 278-293.
Elsevier DOI 2007
Flood, Sentinel-1 SAR, Google Earth Engine, Bangladesh, Sentinel-2 BibRef

Pulvirenti, L.[Luca], Chini, M.[Marco], Pierdicca, N.[Nazzareno],
InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Qiu, J.L.[Jun-Liang], Cao, B.[Bowen], Park, E.[Edward], Yang, X.K.[Xian-Kun], Zhang, W.X.[Wen-Xin], Tarolli, P.[Paolo],
Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Pulvirenti, L.[Luca], Squicciarino, G.[Giuseppe], Fiori, E.[Elisabetta], Ferraris, L.[Luca], Puca, S.[Silvia],
A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Kitajima, N.[Natsumi], Seto, R.[Rie], Yamazaki, D.[Dai], Zhou, X.D.[Xu-Dong], Ma, W.C.[Wen-Chao], Kanae, S.[Shinjiro],
Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Yang, Z.K.[Zhong-Kang], Wei, J.[Jinbing], Deng, J.H.[Jian-Hui], Gao, Y.J.[Yun-Jian], Zhao, S.Y.[Si-Yuan], He, Z.L.[Zhi-Liang],
Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Katiyar, V.[Vaibhav], Tamkuan, N.[Nopphawan], Nagai, M.[Masahiko],
Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Nagai, H.[Hiroto], Abe, T.[Takahiro], Ohki, M.[Masato],
SAR-Based Flood Monitoring for Flatland with Frequently Fluctuating Water Surfaces: Proposal for the Normalized Backscatter Amplitude Difference Index (NoBADI),
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Pertiwi, A.P.[Avi Putri], Roth, A.[Achim], Schaffhauser, T.[Timo], Bhola, P.K.[Punit Kumar], Reuß, F.[Felix], Stettner, S.[Samuel], Kuenzer, C.[Claudia], Disse, M.[Markus],
Monitoring the Spring Flood in Lena Delta with Hydrodynamic Modeling Based on SAR Satellite Products,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Chen, S.[Shujie], Huang, W.L.[Wen-Li], Chen, Y.M.[Yu-Min], Feng, M.[Mei],
An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Sipelgas, L.[Liis], Aavaste, A.[Age], Uiboupin, R.[Rivo],
Mapping Flood Extent and Frequency from Sentinel-1 Imagery during the Extremely Warm Winter of 2020 in Boreal Floodplains and Forests,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Kim, J.[Junwoo], Kim, H.[Hwisong], Jeon, H.Y.[Hyung-Yun], Jeong, S.H.[Seung-Hwan], Song, J.[Juyoung], Vadivel, S.K.P.[Suresh Krishnan Palanisamy], Kim, D.J.[Duk-Jin],
Synergistic Use of Geospatial Data for Water Body Extraction from Sentinel-1 Images for Operational Flood Monitoring across Southeast Asia Using Deep Neural Networks,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Jiang, X.[Xin], Liang, S.J.[Shi-Jing], He, X.Y.[Xin-Yue], Ziegler, A.D.[Alan D.], Lin, P.R.[Pei-Rong], Pan, M.[Ming], Wang, D.S.[Da-Shan], Zou, J.Y.[Jun-Yu], Hao, D.L.[Da-Lei], Mao, G.Q.[Gan-Quan], Zeng, Y.L.[Ye-Lu], Yin, J.[Jie], Feng, L.[Lian], Miao, C.Y.[Chi-Yuan], Wood, E.F.[Eric F.], Zeng, Z.Z.[Zhen-Zhong],
Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning,
PandRS(178), 2021, pp. 36-50.
Elsevier DOI 2108
Flood inundation, Sentinel-1, Unsupervised machine learning, Google Earth Engine, Disaster assessment BibRef

Yoon, S.S.[Seong-Sim], Lim, S.H.[Sang-Hun],
Analyzing the Application of X-Band Radar for Improving Rainfall Observation and Flood Forecasting in Yeongdong, South Korea,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Tiampo, K.F.[Kristy F.], Huang, L.[Lingcao], Simmons, C.[Conor], Woods, C.[Clay], Glasscoe, M.T.[Margaret T.],
Detection of Flood Extent Using Sentinel-1A/B Synthetic Aperture Radar: An Application for Hurricane Harvey, Houston, TX,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Elkhrachy, I.[Ismail],
Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, Z.R.[Zi-Rui], Xie, F.[Fei], Ling, F.[Feng], Du, Y.[Yun],
Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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Foroughnia, F.[Fatemeh], Alfieri, S.M.[Silvia Maria], Menenti, M.[Massimo], Lindenbergh, R.[Roderik],
Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Bauer-Marschallinger, B.[Bernhard], Cao, S.[Senmao], Tupas, M.E.[Mark Edwin], Roth, F.[Florian], Navacchi, C.[Claudio], Melzer, T.[Thomas], Freeman, V.[Vahid], Wagner, W.[Wolfgang],
Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Navacchi, C.[Claudio], Cao, S.[Senmao], Bauer-Marschallinger, B.[Bernhard], Snoeij, P.[Paul], Small, D.[David], Wagner, W.[Wolfgang],
Utilising Sentinel-1's orbital stability for efficient pre-processing of sigma nought backscatter,
PandRS(192), 2022, pp. 130-141.
Elsevier DOI 2209
Sentinel-1, Synthetic Aperture Radar (SAR), Ground Range Detected (GRD), Georeferencing, Orbital tube BibRef

Wu, H.[Han], Song, H.[Huina], Huang, J.H.[Jian-Hua], Zhong, H.[Hua], Zhan, R.H.[Rong-Hui], Teng, X.Y.[Xu-Yang], Qiu, Z.Y.[Zhao-Yang], He, M.[Meilin], Cao, J.Y.[Jia-Yi],
Flood Detection in Dual-Polarization SAR Images Based on Multi-Scale Deeplab Model,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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Tran, K.H.[Khuong H.], Menenti, M.[Massimo], Jia, L.[Li],
Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Lv, S.[Suna], Meng, L.S.[Ling-Sheng], Edwing, D.[Deanna], Xue, S.[Sihan], Geng, X.[Xupu], Yan, X.H.[Xiao-Hai],
High-Performance Segmentation for Flood Mapping of HISEA-1 SAR Remote Sensing Images,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Baghermanesh, S.S.[Shadi Sadat], Jabari, S.[Shabnam], McGrath, H.[Heather],
Urban Flood Detection Using TerraSAR-X and SAR Simulated Reflectivity Maps,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Salem, A.[Abdella], Hashemi-Beni, L.[Leila],
Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Gokon, H.[Hideomi], Endo, F.[Fuyuki], Koshimura, S.[Shunichi],
Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Wu, X.[Xuan], Zhang, Z.J.[Zhi-Jie], Xiong, S.Q.[Sheng-Qing], Zhang, W.C.[Wan-Chang], Tang, J.[Jiakui], Li, Z.H.[Zheng-Hao], An, B.S.[Bang-Sheng], Li, R.[Rui],
A Near-Real-Time Flood Detection Method Based on Deep Learning and SAR Images,
RS(15), No. 8, 2023, pp. 2046.
DOI Link 2305
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Andrew, O.[Ogbaje], Apan, A.[Armando], Paudyal, D.R.[Dev Raj], Perera, K.[Kithsiri],
Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data,
IJGI(12), No. 5, 2023, pp. xx-yy.
DOI Link 2306
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Li, H.[Hengkai], Xu, Z.[Zikun], Zhou, Y.B.[Yan-Bing], He, X.X.[Xiao-Xing], He, M.H.[Ming-Hua],
Flood Monitoring Using Sentinel-1 SAR for Agricultural Disaster Assessment in Poyang Lake Region,
RS(15), No. 21, 2023, pp. 5247.
DOI Link 2311
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Colacicco, R.[Rosa], Refice, A.[Alberto], Nutricato, R.[Raffaele], Bovenga, F.[Fabio], Caporusso, G.[Giacomo], d'Addabbo, A.[Annarita], Salandra, M.L.[Marco La], Lovergine, F.P.[Francesco Paolo], Nitti, D.O.[Davide Oscar], Capolongo, D.[Domenico],
High-Resolution Flood Monitoring Based on Advanced Statistical Modeling of Sentinel-1 Multi-Temporal Stacks,
RS(16), No. 2, 2024, pp. 294.
DOI Link 2402
BibRef

Saleh, T.[Tamer], Weng, X.X.[Xing-Xing], Holail, S.[Shimaa], Hao, C.[Chen], Xia, G.S.[Gui-Song],
DAM-Net: Flood detection from SAR imagery using differential attention metric-based vision transformers,
PandRS(212), 2024, pp. 440-453.
Elsevier DOI Code:
WWW Link. 2406
Flood detection, SAR imagery, S1GFloods dataset, Vision transformers BibRef


de la Cruz, R.M., Olfindo Jr., N.T., Felicen, M.M., Borlongan, N.J.B., Difuntorum, J.K.L., Marciano Jr., J.J.S.,
Near-realtime Flood Detection From Multi-temporal Sentinel Radar Images Using Artificial Intelligence,
ISPRS20(B3:1663-1670).
DOI Link 2012
BibRef

Papila, I., Alganci, U., Sertel, E.,
Sentinel-1 Based Flood Mapping Using Interferometric Coherence And Intensity Change Detection Approach,
ISPRS20(B3:1697-1703).
DOI Link 2012
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Rambour, C., Audebert, N., Koeniguer, E., Le Saux, B., Crucianu, M., Datcu, M.,
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