305 citations as recorded by crossref.

1. Sentinel-1-based flood mapping: a fully automated processing chain A. Twele et al. https://doi.org/10.1080/01431161.2016.1192304
2. A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level K. Yan et al. https://doi.org/10.1002/hyp.10449
3. A methodology for mapping annual flood extent using multi-temporal Sentinel-1 imagery T. McCormack et al. https://doi.org/10.1016/j.rse.2022.113273
4. Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan A. Tariq et al. https://doi.org/10.3390/f12101371
5. OBIA Flood Delimitation Assisted by Threshold Determination with Principal Component Analysis D. Roque et al. https://doi.org/10.14358/PERS.80.6.551-557
6. Brief communication: Hurricane Dorian: automated near-real-time mapping of the “unprecedented” flooding in the Bahamas using synthetic aperture radar D. Cerrai et al. https://doi.org/10.5194/nhess-20-1463-2020
7. The last two decades of computer vision technologies in water resource management: A bibliometric analysis U. Iqbal et al. https://doi.org/10.1111/wej.12845
8. Evaluation of Automated Water Surface Extraction Using Multi-Source Remote Sensing Data: A Case Study of the Veľká Domaša Reservoir, Slovakia Ľ. Kseňak et al. https://doi.org/10.3390/rs18040545
9. Flood period estimation using multi-sensor satellite data: Case study on Punjab floods 2023 Y. Bhageerath et al. https://doi.org/10.1007/s12040-024-02499-6
10. The International Charter ‘Space and Major Disasters’: DLR’s Contributions to Emergency Response Worldwide S. Martinis et al. https://doi.org/10.1007/s41064-017-0032-1
11. Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery I. Hlaváčová et al. https://doi.org/10.3390/w13233392
12. Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP) P. Gautam et al. https://doi.org/10.1007/s12517-024-11910-x
13. Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing J. Kim et al. https://doi.org/10.3390/rs14246373
14. Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data J. Jung et al. https://doi.org/10.1016/j.rse.2026.115326
15. Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study F. Carreño Conde & M. De Mata Muñoz https://doi.org/10.3390/w11122454
16. Automatic extraction of flood inundation areas from SAR images: a case study of Jilin, China during the 2017 flood disaster L. Wan et al. https://doi.org/10.1080/01431161.2019.1577999
17. A novel spectral and phenological composite index for the early automatic mapping of potatoes from Sentinel-2 multi-temporal images L. Wang et al. https://doi.org/10.1016/j.compag.2026.111459
18. A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery F. Bioresita et al. https://doi.org/10.3390/rs10020217
19. Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion Y. Li et al. https://doi.org/10.3390/rs11192231
20. Flood Inundation Distribution In Lamongan Regency Using Sentinel-1 SAR Data And Automatic Water-S1 Method L. Azizah et al. https://doi.org/10.1088/1755-1315/1418/1/012052
21. The use of remote sensing to characterise hydromorphological properties of European rivers S. Bizzi et al. https://doi.org/10.1007/s00027-015-0430-7
22. An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery J. Li & S. Wang https://doi.org/10.1080/01431161.2015.1009653
23. A fully automated TerraSAR-X based flood service S. Martinis et al. https://doi.org/10.1016/j.isprsjprs.2014.07.014
24. Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations E. Oliveira et al. https://doi.org/10.3390/rs11111305
25. CISRMamba: Cross-Modal Interaction and Scan-Routing Mamba for Multi-Sensor Flood Inundation Mapping H. Feng et al. https://doi.org/10.3390/s26165224
26. Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands A. Hardy et al. https://doi.org/10.3390/rs12071182
27. Enabling the Use of Earth Observation Data for Integrated Water Resource Management in Africa with the Water Observation and Information System R. Guzinski et al. https://doi.org/10.3390/rs6087819
28. Snow Avalanche Debris Analysis Using Time Series of Dual-Polarimetric Synthetic Aperture Radar Data S. Schlaffer & M. Schlögl https://doi.org/10.1109/JSTARS.2024.3423403
29. Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping F. Bioresita et al. https://doi.org/10.1080/01431161.2019.1624869
30. Flood Extent Mapping in the Caprivi Floodplain Using Sentinel-1 Time Series T. Bangira et al. https://doi.org/10.1109/JSTARS.2021.3083517
31. Brief communication: Key papers of 20 years in Natural Hazards and Earth System Sciences A. Gain et al. https://doi.org/10.5194/nhess-22-985-2022
32. Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies F. Xue et al. https://doi.org/10.1109/JSTARS.2022.3176388
33. Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion M. Ouled Sghaier et al. https://doi.org/10.3390/rs10020237
34. Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas H. Cao et al. https://doi.org/10.3390/w11040786
35. Inundation Extent Mapping by Synthetic Aperture Radar: A Review X. Shen et al. https://doi.org/10.3390/rs11070879
36. A review of remote sensing applications for water security: Quantity, quality, and extremes I. Chawla et al. https://doi.org/10.1016/j.jhydrol.2020.124826
37. FM-Mamba: end-to-end non-causal Mamba-based network for efficient flood mapping B. Wang et al. https://doi.org/10.1038/s41598-026-56046-y
38. Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning V. Katiyar et al. https://doi.org/10.3390/rs13122334
39. A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block J. Tan et al. https://doi.org/10.3390/rs15102690
40. Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: a case of Dar es Salaam B. Demissie et al. https://doi.org/10.1080/19475705.2023.2202296
41. A Novel Fully Automated Mapping of the Flood Extent on SAR Images Using a Supervised Classifier A. Benoudjit & R. Guida https://doi.org/10.3390/rs11070779
42. Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation L. Pulvirenti et al. https://doi.org/10.1016/j.rse.2010.12.002
43. Observing floods from space: Experience gained from COSMO-SkyMed observations N. Pierdicca et al. https://doi.org/10.1016/j.actaastro.2012.10.034
44. RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1 G. Oakes et al. https://doi.org/10.3390/rs15061705
45. A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico N. Chaouch et al. https://doi.org/10.1002/hyp.8268
46. Flooding Water Depth Estimation With High-Resolution SAR P. Iervolino et al. https://doi.org/10.1109/TGRS.2014.2358501
47. Multiscale framework for rapid change analysis from SAR image time series: Case study of flood monitoring in the central coast regions of Vietnam T. Lê et al. https://doi.org/10.1016/j.rse.2021.112837
48. Extraction and Classification of Flood-Affected Areas Based on MRF and Deep Learning J. Wang et al. https://doi.org/10.3390/w15071288
49. Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part I: Comparison and Analysis Y. Xia et al. https://doi.org/10.1175/JHM-D-14-0096.1
50. Flood Inundation Probability Estimation by Integrating Physical and Social Sensing Data: Case Study of 2021 Heavy Rainfall in Henan, China W. Du et al. https://doi.org/10.3390/rs16152734
51. Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java, Indonesia F. Yulianto et al. https://doi.org/10.1007/s11069-015-1633-x
52. Assimilating SAR-derived water level data into a hydraulic model: a case study L. Giustarini et al. https://doi.org/10.5194/hess-15-2349-2011
53. Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm S. Yang et al. https://doi.org/10.1016/j.scitotenv.2024.174329
54. Distilling Intelligence from Space: Lightweight Deep Learning for SAR-Based Flood Monitoring P. Thongsang et al. https://doi.org/10.1007/s41748-026-01146-y
55. Mapping Flooded Vegetation Using COSMO-SkyMed: Comparison With Polarimetric and Optical Data Over Rice Fields N. Pierdicca et al. https://doi.org/10.1109/JSTARS.2017.2711960
56. An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data L. Pulvirenti et al. https://doi.org/10.3390/rs12040674
57. Flood Damage Assessment Through Multitemporal COSMO-SkyMed Data and Hydrodynamic Models: The Albania 2010 Case Study L. Pulvirenti et al. https://doi.org/10.1109/JSTARS.2014.2328012
58. Deep Learning Ensemble for Flood Probability Analysis F. Sseguya & K. Jun https://doi.org/10.3390/w16213092
59. RadWet-L: A Novel Approach for Mapping of Inundation Dynamics of Forested Wetlands Using ALOS-2 PALSAR-2 L-Band Radar Imagery G. Oakes et al. https://doi.org/10.3390/rs16122078
60. The Use of C-Band and X-Band SAR with Machine Learning for Detecting Small-Scale Mining G. Janse van Rensburg & J. Kemp https://doi.org/10.3390/rs14040977
61. Resilience to unusual flooding after 2021 tropical storms in part of mainland Southeast Asia P. Wattanachareekul et al. https://doi.org/10.3389/fevo.2022.1072993
62. Towards high resolution flood monitoring: An integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery Z. Zeng et al. https://doi.org/10.1016/j.jhydrol.2019.124377
63. Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps D. Mason et al. https://doi.org/10.3390/w13111577
64. An integrated data-driven approach using dual polarized SAR data for spatiotemporal analysis of water surface changes S. Bagheri et al. https://doi.org/10.1016/j.asr.2025.09.018
65. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis J. Teng et al. https://doi.org/10.1016/j.envsoft.2017.01.006
66. The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Hara et al. https://doi.org/10.2478/ijafr-2019-0006
67. Revealing upstream dam operations under information asymmetry for integrated water resources management K. Han et al. https://doi.org/10.1088/1748-9326/ae9967
68. Optimized Rule-Based Flood Mapping Technique Using Multitemporal RADARSAT-2 Images in the Tropical Region B. Pradhan et al. https://doi.org/10.1109/JSTARS.2017.2676343
69. Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data F. Yulianto et al. https://doi.org/10.1155/2022/4894929
70. Improving Flood Detection in the Himalayas: Evaluating SAR-Optical Data Fusion with Machine Learning in a Mountainous River Basin of Nepal S. Rijal et al. https://doi.org/10.1007/s12524-026-02484-0
71. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston F. Brill et al. https://doi.org/10.3390/rs13112042
72. A Multi-Sensor Exportable Approach for Automatic Flooded Areas Detection and Monitoring by a Composite Satellite Constellation M. Faruolo et al. https://doi.org/10.1109/TGRS.2012.2236336
73. The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas S. Martinis et al. https://doi.org/10.3390/rs10040583
74. TanDEM-X Water Indication Mask: Generation and First Evaluation Results A. Wendleder et al. https://doi.org/10.1109/JSTARS.2012.2210999
75. Flood extent mapping for Namibia using change detection and thresholding with SAR S. Long et al. https://doi.org/10.1088/1748-9326/9/3/035002
76. Automated Extraction of Inundated Areas from Multi-Temporal Dual-Polarization RADARSAT-2 Images of the 2011 Central Thailand Flood P. Nakmuenwai et al. https://doi.org/10.3390/rs9010078
77. Evaluation of Sentinel-1 data for flood mapping in the upstream of Sidi Salem dam (Northern Tunisia) A. Ezzine et al. https://doi.org/10.1007/s12517-018-3505-7
78. Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images W. Liu et al. https://doi.org/10.3390/rs13040639
79. Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery G. Ruzza et al. https://doi.org/10.3390/w11112289
80. Flood hazard assessment of August 20, 2016 floods in Satna District, Madhya Pradesh, India R. Kumar et al. https://doi.org/10.1016/j.rsase.2018.06.001
81. Estimating Ensemble Likelihoods for the Sentinel-1-Based Global Flood Monitoring Product of the Copernicus Emergency Management Service C. Krullikowski et al. https://doi.org/10.1109/JSTARS.2023.3292350
82. Integrated Flood Impact and Vulnerability Assessment Using a Multi-Sensor Earth Observation Mission with the Perspective of an Operational Service in Lombardy, Italy M. Righini et al. https://doi.org/10.3390/land13020140
83. Remote Sensing of Environmental Drivers Influencing the Movement Ecology of Sympatric Wild and Domestic Ungulates in Semi-Arid Savannas, a Review F. Rumiano et al. https://doi.org/10.3390/rs12193218
84. Extending general compact querieable representations to GIS applications N. Brisaboa et al. https://doi.org/10.1016/j.ins.2019.08.007
85. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine B. DeVries et al. https://doi.org/10.1016/j.rse.2020.111664
86. Đánh giá tác động ngập úng diện rộng ảnh hưởng đến việc sử dụng đất nông nghiệp trên địa bàn xã Đại Huệ, tỉnh Nghệ An bằng công nghệ GIS và viễn thám G. Lê et al. https://doi.org/10.67484/mgc.104
87. Implications of boreal forest stand characteristics for X-band SAR flood mapping accuracy J. Cohen et al. https://doi.org/10.1016/j.rse.2016.08.016
88. Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs S. Martinis et al. https://doi.org/10.1109/TGRS.2010.2052816
89. Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin N. Hong Quang et al. https://doi.org/10.3390/w12010071
90. The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data A. Tavakol et al. https://doi.org/10.1016/j.rsase.2021.100649
91. Estimating the impact of satellite observations on the predictability of large-scale hydraulic models K. Andreadis & G. Schumann https://doi.org/10.1016/j.advwatres.2014.06.006
92. Automatic near real-time flood detection using Suomi-NPP/VIIRS data S. Li et al. https://doi.org/10.1016/j.rse.2017.09.032
93. HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya F. Meyer et al. https://doi.org/10.3390/rs16173244
94. Automatic Surface Water Mapping Using Polarimetric SAR Data for Long-Term Change Detection W. Zhang et al. https://doi.org/10.3390/w12030872
95. Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses C. Kuenzer et al. https://doi.org/10.3390/rs5020687
96. Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India M. Rahman & P. Thakur https://doi.org/10.1016/j.ejrs.2017.10.002
97. A New Short-Wave Infrared (SWIR) Method for Quantitative Water Fraction Derivation and Evaluation With EOS/MODIS and Landsat/TM Data S. Li et al. https://doi.org/10.1109/TGRS.2012.2208466
98. Flood detection from multi-temporal SAR data using harmonic analysis and change detection S. Schlaffer et al. https://doi.org/10.1016/j.jag.2014.12.001
99. Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies P. Matgen et al. https://doi.org/10.1016/j.pce.2010.12.009
100. DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions . Manavalan & . Rao https://doi.org/10.1080/10106049.2013.838310
101. Change detection in floodable areas of the Danube delta using radar images S. Niculescu et al. https://doi.org/10.1007/s11069-015-1809-4
102. Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection C. Tottrup et al. https://doi.org/10.3390/rs14102410
103. The fully-automatic Sentinel-1 Global Flood Monitoring service: Scientific challenges and future directions W. Wagner et al. https://doi.org/10.1016/j.rse.2025.115108
104. Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data V. Gstaiger et al. https://doi.org/10.1080/01431161.2012.700421
105. Operational SAR flood mapping as a full-stack systems problem: an AI-enabled perspective Q. Yang https://doi.org/10.3389/frwa.2026.1871753
106. Assessment of time-series-derived no-flood references for sar-based Bayesian flood mapping M. Tupas et al. https://doi.org/10.1080/15481603.2024.2427304
107. A MODIS-based automated flood monitoring system for southeast asia A. Ahamed & J. Bolten https://doi.org/10.1016/j.jag.2017.05.006
108. Computerized Seed and Range Selection Method for Flood Extent Extraction in SAR Image Using Iterative Region Growing A. Chakraborty & D. Chakraborty https://doi.org/10.1007/s12524-018-0906-8
109. Spatial Evaluation of a Natural Flood Management Project Using SAR Change Detection S. Jarrett & D. Hölbling https://doi.org/10.3390/w15122182
110. Automated Extraction of Surface Water Extent from Sentinel-1 Data W. Huang et al. https://doi.org/10.3390/rs10050797
111. Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery S. Plank et al. https://doi.org/10.1080/01431161.2017.1306143
112. SemiCD-BG: disaster detection and anomaly intrusion detection in railroad operation using change detection G. Wen et al. https://doi.org/10.1080/29941849.2025.2572569
113. Examining Community Vulnerabilities through multi-scale geospatial analysis of social media activity during Hurricane Irma A. Forati & R. Ghose https://doi.org/10.1016/j.ijdrr.2021.102701
114. Urban Flood Detection Using TerraSAR-X and SAR Simulated Reflectivity Maps S. Baghermanesh et al. https://doi.org/10.3390/rs14236154
115. Mapping the 2021 October Flood Event in the Subsiding Taiyuan Basin by Multitemporal SAR Data H. Feng et al. https://doi.org/10.1109/JSTARS.2022.3204277
116. Object-Based Flood Analysis Using a Graph-Based Representation B. Debusscher & F. Van Coillie https://doi.org/10.3390/rs11161883
117. DEM Extraction in Urban Areas Using High-Resolution TerraSAR-X Imagery U. Sefercik et al. https://doi.org/10.1007/s12524-013-0317-9
118. Flash floods in traditionally less flood prone areas of southeastern Bangladesh M. Hossain et al. https://doi.org/10.1007/s44288-026-00752-7
119. Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches L. Landuyt et al. https://doi.org/10.1109/TGRS.2018.2860054
120. Flooded Rice Paddy Detection Using Sentinel-1 and PlanetScope Data: A Case Study of the 2018 Spring Flood in West Java, Indonesia H. Wakabayashi et al. https://doi.org/10.1109/JSTARS.2021.3083610
121. Water extraction from SAR images based on improved geodesic active contour J. Wan et al. https://doi.org/10.1007/s10661-022-10366-4
122. Monitoring of “7-20” Rainstorm Damage in the Zhengzhou Road Network Using Heterogeneous SAR Images S. Li et al. https://doi.org/10.3390/app13021103
123. Detection of Water Bodies from Kompsat-5 SAR Data S. Park https://doi.org/10.7780/kjrs.2016.32.5.11
124. Weakly Supervised Deep Soft Clustering for Flood Identification in SAR Images F. Ma et al. https://doi.org/10.1109/LGRS.2022.3150778
125. Machine Learning Based Method for Detecting Tsunami Devastated Area Using TerraSAR-X Data H. GOKON et al. https://doi.org/10.2208/kaigan.69.I_1441
126. An active monitoring method for flood events Z. Chen et al. https://doi.org/10.1016/j.cageo.2018.04.009
127. A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X L. Giustarini et al. https://doi.org/10.1109/TGRS.2012.2210901
128. Detection of urban flood inundation using RISAT-1 SAR images: a case study of Srinagar, Jammu and Kashmir (North India) floods of September 2014 C. Bhatt et al. https://doi.org/10.1007/s40808-019-00690-z
129. Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data M. Rättich et al. https://doi.org/10.3390/rs12040643
130. Development of algorithms for evaluating performance of flood simulation models with satellite-derived flood T. Surwase et al. https://doi.org/10.2166/h2oj.2020.117
131. An unusually long Rift valley fever inter-epizootic period in Zambia: Evidence for enzootic virus circulation and risk for disease outbreak H. Chambaro et al. https://doi.org/10.1371/journal.pntd.0010420
132. A Physics-Guided Neural Network for Flooding Area Detection Using SAR Imagery and Local River Gauge Observations M. Gierszewska & T. Berezowski https://doi.org/10.1109/JSTARS.2025.3561926
133. Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks W. Kang et al. https://doi.org/10.3390/s18092915
134. Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine M. Singha et al. https://doi.org/10.1016/j.isprsjprs.2020.06.011
135. Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands H. Ban et al. https://doi.org/10.3390/rs9040313
136. Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets Y. Bai et al. https://doi.org/10.3390/rs13112220
137. Flood Modeling and Prediction Using Earth Observation Data G. Schumann et al. https://doi.org/10.1007/s10712-022-09751-y
138. Comparing four operational SAR-based water and flood detection approaches S. Martinis et al. https://doi.org/10.1080/01431161.2015.1060647
139. Dataset for Water Body Detection Using Satellite SAR Images S. Lee & H. Oh https://doi.org/10.22761/DJ2021.3.2.002
140. A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data L. Pulvirenti et al. https://doi.org/10.3390/rs13071342
141. Leveraging synthetic assimilation of remote sensing with the National Water Model (NWM) to improve above-normal flow prediction in ungauged basins S. Fang et al. https://doi.org/10.1088/1748-9326/ad8808
142. A novel change homogenizing flood index and an expedited locally tuned Markov random field model for unsupervised rural floodwater detection in sentinel-1 data: An application to flood impact assessment A. Mohsenifar et al. https://doi.org/10.1016/j.isprsjprs.2026.08.021
143. Ambulance STARS: A Satellite-Driven Framework for Rapid Flood Impact Assessment and Time-Critical Ambulance Routing M. Lupa et al. https://doi.org/10.3390/rs18173004
144. Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece D. Alexakis et al. https://doi.org/10.1080/10106049.2018.1434685
145. Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey M. Goldberg et al. https://doi.org/10.3390/rs10081256
146. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. https://doi.org/10.3390/rs16040656
147. Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. https://doi.org/10.3390/ijerph19010237
148. Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask O. Kavats et al. https://doi.org/10.3390/w14244030
149. Flood mapping in SAR images via threshold segmentation with hydrological-hydrodynamic modeling Z. Li et al. https://doi.org/10.1016/j.jhydrol.2025.134825
150. Near-real-time non-obstructed flood inundation mapping using synthetic aperture radar X. Shen et al. https://doi.org/10.1016/j.rse.2018.11.008
151. Application of Gated Recurrent Unit Neural Network for Flood Extraction from Synthetic Aperture Radar Time Series M. Zhang et al. https://doi.org/10.3390/w15213779
152. Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India S. Surampudi & K. Yarrakula https://doi.org/10.1007/s11356-019-06849-6
153. Estimation of Flood Inundation and Depth During Hurricane Florence Using Sentinel-1 and UAVSAR Data S. Kundu et al. https://doi.org/10.1109/LGRS.2022.3165444
154. Automatic procedures analyzing remote sensing data to minimize flood response time: a step towards National flood mapping service V. Sharma et al. https://doi.org/10.1007/s41324-017-0132-4
155. Monitoring Flood Evolution in Vegetated Areas Using COSMO-SkyMed Data: The Tuscany 2009 Case Study L. Pulvirenti et al. https://doi.org/10.1109/JSTARS.2012.2219509
156. Evaluating the impact of flood inundation with the cloud computing platform over vegetation cover of Ganga Basin during COVID-19 S. Ghosh et al. https://doi.org/10.1007/s41324-022-00430-z
157. Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine V. Tiwari et al. https://doi.org/10.1371/journal.pone.0237324
158. Earth observation data based rapid flood-extent modelling for tsunami-devastated coastal areas S. Hese & T. Heyer https://doi.org/10.1016/j.jag.2015.11.005
159. The potential of Sentinel-1 imagery for flood event detection: A satellite vs. hydraulic model comparison J. Breznik et al. https://doi.org/10.1016/j.jhydrol.2024.132587
160. SAR-Based Flood Extent Mapping with a Lightweight Siamese U-Net and Differential Attention Mechanism A. Kaçmaz & U. Alganci https://doi.org/10.3390/earth7030087
161. Flood mapping under vegetation using single SAR acquisitions S. Grimaldi et al. https://doi.org/10.1016/j.rse.2019.111582
162. Towards developing comparable optical and SAR remote sensing inundation mapping with hydrodynamic modelling C. Ticehurst & F. Karim https://doi.org/10.1080/01431161.2023.2211714
163. A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact S. Van Ackere et al. https://doi.org/10.3390/w11112275
164. A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet Y. Zhang et al. https://doi.org/10.1080/17538947.2023.2230978
165. Assessing Flood Vulnerability of Landfills in Southern New Jersey: Incorporating Climate Change and Extreme Weather Impacts R. Chowdhury et al. https://doi.org/10.3390/w18091085
166. Microwave remote sensing of flood inundation G. Schumann & D. Moller https://doi.org/10.1016/j.pce.2015.05.002
167. Enhancing precision of flood estimation in EOS-04 SAR imagery: a statistical approach Y. Bhageerath et al. https://doi.org/10.1080/19475705.2024.2413910
168. Assessing seasonal dynamics of surface water using multitemporal SAR data M. Islam et al. https://doi.org/10.1007/s12518-026-00709-y
169. Self-Supervised Reservoir Water Area Detection Across Multi-Source Optical Imagery G. Mo et al. https://doi.org/10.3390/rs18060918
170. SAR-based flood detection and different ensemble boosting techniques for multi-factor flood susceptibility modelling: a case study in Uttar Dinajpur, West Bengal, India P. Mitra et al. https://doi.org/10.1007/s11069-026-08171-0
171. A large-scale 2005–2012 flood map record derived from ENVISAT-ASAR data: United Kingdom as a test case J. Zhao et al. https://doi.org/10.1016/j.rse.2021.112338
172. Use of Sentinel-1 GRD SAR Images to Delineate Flood Extent in Pakistan M. Zhang et al. https://doi.org/10.3390/su12145784
173. On the Exploitation of Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions Q. Zhao et al. https://doi.org/10.3390/rs14102384
174. Detecting Flooded Areas Using Sentinel-1 SAR Imagery F. Alonso-Sarria et al. https://doi.org/10.3390/rs17081368
175. Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images C. Cazals et al. https://doi.org/10.3390/rs8070570
176. Operational Surface Water Detection and Monitoring Using Radarsat 2 S. Bolanos et al. https://doi.org/10.3390/rs8040285
177. A Framework to Assess Remote Sensing Algorithms for Satellite-Based Flood Index Insurance M. Thomas et al. https://doi.org/10.1109/JSTARS.2023.3244098
178. Extracting urban water bodies from high-resolution radar images: Measuring the urban surface morphology to control for radar’s double-bounce effect H. Liao & T. Wen https://doi.org/10.1016/j.jag.2019.102003
179. Flood Forecasting in Large River Basins Using FOSS Tool and HPC U. Dutta et al. https://doi.org/10.3390/w13243484
180. Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas S. Hu et al. https://doi.org/10.3390/rs12020243
181. Probabilistic mapping of flood-induced backscatter changes in SAR time series S. Schlaffer et al. https://doi.org/10.1016/j.jag.2016.12.003
182. Extreme rainfall-induced urban flood monitoring and damage assessment in Wuhan (China) and Kumamoto (Japan) cities using Google Earth Engine A. Pandey et al. https://doi.org/10.1007/s10661-022-10076-x
183. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery J. Liang & D. Liu https://doi.org/10.1016/j.isprsjprs.2019.10.017
184. Where is my attention? An explainable AI exploration in water detection from SAR imagery L. Chen et al. https://doi.org/10.1016/j.jag.2024.103878
185. Flood Mapping With TerraSAR-X in Forested Regions in Estonia K. Voormansik et al. https://doi.org/10.1109/JSTARS.2013.2283340
186. Integration of SAR and multi-spectral imagery in flood inundation mapping – a case study on Kerala floods 2018 J. Jacinth Jennifer et al. https://doi.org/10.1080/09715010.2020.1791265
187. On Flood Detection Using Dual-Polarimetric SAR Observation S. Kim et al. https://doi.org/10.3390/rs17111931
188. A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data E. Pedzisai et al. https://doi.org/10.1016/j.heliyon.2023.e13332
189. A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness J. Eudaric et al. https://doi.org/10.1038/s41598-024-69977-1
190. The Synergistic Use of RADARSAT-2 Ascending and Descending Images to Improve Surface Water Detection Accuracy in Alberta, Canada E. DeLancey et al. https://doi.org/10.1080/07038992.2019.1691516
191. Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning X. Jiang et al. https://doi.org/10.1016/j.isprsjprs.2021.05.019
192. Flood Hazard Mapping Combining Hydrodynamic Modeling and Multi Annual Remote Sensing data L. Giustarini et al. https://doi.org/10.3390/rs71014200
193. Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery W. Liu & F. Yamazaki https://doi.org/10.5194/nhess-18-1905-2018
194. Discrimination of Water Surfaces, Heavy Rainfall, and Wet Snow Using COSMO-SkyMed Observations of Severe Weather Events L. Pulvirenti et al. https://doi.org/10.1109/TGRS.2013.2244606
195. Sentetik Açıklıklı Radar verilerinin Taşkın Çalışmalarında Kullanılması: Berdan Ovası Taşkını M. AKGÜL https://doi.org/10.29128/geomatik.378123
196. Inland Surface Waters Quantity Monitored from Remote Sensing J. Cretaux et al. https://doi.org/10.1007/s10712-023-09803-x
197. Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data K. Brown et al. https://doi.org/10.1177/0309133316633570
198. Towards Operational Flood Monitoring in Flanders Using Sentinel-1 L. Landuyt et al. https://doi.org/10.1109/JSTARS.2021.3121992
199. Identifying the effect of monsoon floods on vegetation and land surface temperature by using Google Earth Engine S. Rahaman & N. Shermin https://doi.org/10.1016/j.uclim.2022.101162
200. Flood depth estimation by means of high-resolution SAR images and lidar data F. Cian et al. https://doi.org/10.5194/nhess-18-3063-2018
201. Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case of the 2015 Heavy Rainfall in Japan M. Ohki et al. https://doi.org/10.1109/JSTARS.2019.2911596
202. Monitoring River Basin Development and Variation in Water Resources in Transboundary Imjin River in North and South Korea Using Remote Sensing D. Kim et al. https://doi.org/10.3390/rs12010195
203. Monitoring surface water dynamics in the Prairie Pothole Region of North Dakota using dual-polarised Sentinel-1 synthetic aperture radar (SAR) time series S. Schlaffer et al. https://doi.org/10.5194/hess-26-841-2022
204. Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery E. Nemni et al. https://doi.org/10.3390/rs12162532
205. Hydrologic regime dynamics and driving factors of Anglaren Co on the Tibetan Plateau based on multi-source remote sensing data D. Huang et al. https://doi.org/10.1016/j.jhydrol.2025.133645
206. Automatic flood detection using sentinel-1 images on the google earth engine M. Moharrami et al. https://doi.org/10.1007/s10661-021-09037-7
207. Beyond Major Floods: Deep Learning for Detecting Shallow Water Inundation in Agricultural Areas P. Konrad et al. https://doi.org/10.1016/j.procs.2025.09.149
208. Robust algorithm for detecting floodwater in urban areas using synthetic aperture radar images D. Mason et al. https://doi.org/10.1117/1.JRS.12.045011
209. Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series C. Kuenzer et al. https://doi.org/10.3390/rs70708516
210. L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates Z. Kugler et al. https://doi.org/10.3390/rs11070835
211. Satellite Imagery-Based Identification of High-Risk Areas of Schistosome Intermediate Snail Hosts Spread after Flood J. Qiu et al. https://doi.org/10.3390/rs14153707
212. A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data A. D'Addabbo et al. https://doi.org/10.1109/TGRS.2016.2520487
213. The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods G. Schumann et al. https://doi.org/10.1016/j.rse.2011.04.039
214. A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case M. Chini et al. https://doi.org/10.1109/TGRS.2017.2737664
215. River flood mapping in urban areas combining Radarsat-2 data and flood return period data M. Tanguy et al. https://doi.org/10.1016/j.rse.2017.06.042
216. Evaluating the effects of preprocessing, method selection, and hyperparameter tuning on SAR-based flood mapping and water depth estimation J. Travert et al. https://doi.org/10.5194/nhess-26-2387-2026
217. AI-based flood mapping from high-resolution ASNARO-2 images: case study of a severe event in the Center of Vietnam N. Quang et al. https://doi.org/10.1007/s11069-025-07485-9
218. Automatic boosted flood mapping from satellite data B. Coltin et al. https://doi.org/10.1080/01431161.2016.1145366
219. Adaptive water delineation algorithms for L- and C-band SAR imagery: a comparative analysis A. Gujrati et al. https://doi.org/10.1007/s12145-024-01417-0
220. Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images D. Amitrano et al. https://doi.org/10.1109/TGRS.2018.2797536
221. Near Real-Time Flood Detection in Urban and Rural Areas Using High-Resolution Synthetic Aperture Radar Images D. Mason et al. https://doi.org/10.1109/TGRS.2011.2178030
222. MMFlood: A Multimodal Dataset for Flood Delineation From Satellite Imagery F. Montello et al. https://doi.org/10.1109/ACCESS.2022.3205419
223. Taşkın Modelleme Yöntemlerinin Gözden Geçirilmesi ve Karşılaştırılması V. DEMİR et al. https://doi.org/10.31590/ejosat.1010220
224. Amplified deviation flood index (ADFI) for fast non-prior flood detection H. Zhang et al. https://doi.org/10.1016/j.rse.2026.115258
225. Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence L. Pulvirenti et al. https://doi.org/10.1109/TGRS.2015.2482001
226. Evaluation of a new 18-year MODIS-derived surface water fraction dataset for constructing Mediterranean wetland open surface water dynamics L. Li et al. https://doi.org/10.1016/j.jhydrol.2020.124956
227. Development of an Automated Tool for Delineation of Flood Footprints from SAR Imagery for Rapid Disaster Response: A Case Study S. Kuntla & P. Manjusree https://doi.org/10.1007/s12524-020-01125-4
228. Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case M. Chini et al. https://doi.org/10.3390/rs11020107
229. Location-Aware, Context-Driven QoS for IoT Applications E. Ahmad et al. https://doi.org/10.1109/JSYST.2019.2893913
230. Radar Imaging With India’s Earth Observation Satellite-04: Earth resource monitoring using C-band synthetic aperture radar system C. Rao et al. https://doi.org/10.1109/MGRS.2025.3556395
231. Breaking Limits of Remote Sensing by Deep Learning From Simulated Data for Flood and Debris-Flow Mapping N. Yokoya et al. https://doi.org/10.1109/TGRS.2020.3035469
232. EfficientSARNet: A Lightweight, High-Performance, and Fast SAR Image Target Recognition Network Y. Hou et al. https://doi.org/10.1109/JSEN.2026.3666203
233. An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery X. Tong et al. https://doi.org/10.1016/j.isprsjprs.2017.11.006
234. A Curvature-Aware Enhancement and Geometrically Constrained Growing Method for Narrow River Extraction From TG2-InIRA Imagery Y. Li et al. https://doi.org/10.1109/JSTARS.2026.3688685
235. Utilization of Satellite-based Digital Elevation Model (DEM) for Hydrologic Applications: A Review S. Zaidi et al. https://doi.org/10.1007/s12594-018-1016-5
236. Shrimp Ponds Mapping Using Sentinel-1 and Sentinel-2 Data: A Pilot Study in Tumbes, Peru A. Batkalova et al. https://doi.org/10.3390/ijgi15100440
237. Information Extraction From Remote Sensing Images for Flood Monitoring and Damage Evaluation S. Serpico et al. https://doi.org/10.1109/JPROC.2012.2198030
238. Probabilistic Flood Mapping Using Synthetic Aperture Radar Data L. Giustarini et al. https://doi.org/10.1109/TGRS.2016.2592951
239. Cloud-independent flood mapping and surface water dynamics using Sentinel-1 SAR in the Lower Volta Basin, Ghana G. Amoah https://doi.org/10.1016/j.geomat.2026.100131
240. XFIMNet: an Explainable deep learning architecture for versatile flood inundation mapping with synthetic aperture radar and multi-spectral optical images J. Sanderson et al. https://doi.org/10.1080/01431161.2023.2288945
241. A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains S. Martinis et al. https://doi.org/10.3390/rs5115598
242. Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy A. Rangoonwala et al. https://doi.org/10.1080/01431161.2016.1163748
243. Mapping and monitoring of spatio-temporal land use and land cover changes and relationship with normalized satellite indices and driving factors S. Wahla et al. https://doi.org/10.1080/24749508.2023.2187567
244. Level set model for water region segmentation in synthetic aperture radar images W. Lyu et al. https://doi.org/10.1117/1.JRS.13.026510
245. Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique M. Nhangumbe et al. https://doi.org/10.3390/ijgi12020053
246. Multi resolution wavelet attention deep networks for real-time flood detection in the Cauveri River Basin S. Lachure & A. Tiwari https://doi.org/10.15672/hujms.1855645
247. A Prototype System for Flood Monitoring Based on Flood Forecast Combined With COSMO-SkyMed and Sentinel-1 Data G. Boni et al. https://doi.org/10.1109/JSTARS.2016.2514402
248. A Collection of SAR Methodologies for Monitoring Wetlands L. White et al. https://doi.org/10.3390/rs70607615
249. Technical Note: Advances in flash flood monitoring using unmanned aerial vehicles (UAVs) M. Perks et al. https://doi.org/10.5194/hess-20-4005-2016
250. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges S. Grimaldi et al. https://doi.org/10.1007/s10712-016-9378-y
251. Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats A. Hardy et al. https://doi.org/10.3390/rs11050593
252. Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco B. Benzougagh et al. https://doi.org/10.1007/s40996-021-00683-y
253. Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery W. Chen et al. https://doi.org/10.1016/j.jag.2025.104550
254. Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study D. Mason et al. https://doi.org/10.1016/j.rse.2012.06.017
255. A multitemporal index for the automatic identification of winter wheat based on Sentinel-2 imagery time series Y. Xie et al. https://doi.org/10.1080/15481603.2023.2262833
256. Mapping African wetlands for 2020 using multiple spectral, geo-ecological features and Google Earth Engine A. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.09.009
257. Detection of Flooded Areas Caused by Typhoon Hagibis by Applying a Learning-Based Method Using Sentinel-1 Data T. Igarashi & H. Wakabayashi https://doi.org/10.1109/JSTARS.2024.3400282
258. Flood Inundation Extraction and its Impact on Ground Subsidence Using Sentinel-1 Data: A Case Study of the “7.20” Rainstorm Event in Henan Province, China Q. Lan et al. https://doi.org/10.1109/JSTARS.2023.3348845
259. Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data E. Vandebroek et al. https://doi.org/10.3390/rs9070653
260. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data G. Kordelas et al. https://doi.org/10.3390/rs10060910
261. Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine J. Cui et al. https://doi.org/10.3390/agronomy13020355
262. Locating flood embankments using SAR time series: A proof of concept M. Wood et al. https://doi.org/10.1016/j.jag.2018.04.003
263. SAR image analysis techniques for flood area mapping - literature survey R. Manavalan https://doi.org/10.1007/s12145-016-0274-2
264. Performance Study of Landslide Detection Using Multi-Temporal SAR Images Y. Lin et al. https://doi.org/10.3390/rs14102444
265. Automated Processing for Flood Area Detection Using ALOS-2 and Hydrodynamic Simulation Data M. Ohki et al. https://doi.org/10.3390/rs12172709
266. Assessing Single-Polarization and Dual-Polarization TerraSAR-X Data for Surface Water Monitoring K. Irwin et al. https://doi.org/10.3390/rs10060949
267. Extraction of Inundation Areas Due to the July 2018 Western Japan Torrential Rain Event Using Multi-Temporal ALOS-2 Images W. Liu et al. https://doi.org/10.20965/jdr.2019.p0445
268. A Hierarchical Spatio-Temporal Markov Model for Improved Flood Mapping Using Multi-Temporal X-Band SAR Data S. Martinis & A. Twele https://doi.org/10.3390/rs2092240
269. Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms M. Majeed et al. https://doi.org/10.3389/fenvs.2022.1037547
270. GEE4FLOOD: rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth Engine cloud platform V. Vanama et al. https://doi.org/10.1117/1.JRS.14.034505
271. Flood Extent Mapping During Hurricane Florence With Repeat‐Pass L‐Band UAVSAR Images C. Wang et al. https://doi.org/10.1029/2021WR030606
272. RADARSAT-2 Beam Mode Selection for Surface Water and Flooded Vegetation Mapping L. White et al. https://doi.org/10.1080/07038992.2014.943393
273. Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany S. Martinis & C. Rieke https://doi.org/10.3390/rs70607732
274. Analysis of Environmental and Atmospheric Influences in the Use of SAR and Optical Imagery from Sentinel-1, Landsat-8, and Sentinel-2 in the Operational Monitoring of Reservoir Water Level W. Souza et al. https://doi.org/10.3390/rs14092218
275. A highly automated algorithm for wetland detection using multi-temporal optical satellite data C. Ludwig et al. https://doi.org/10.1016/j.rse.2019.01.017
276. Detection and Refinement of Water Bodies Using a Local Thresholding Approach with Feature Extraction from Radar Satellite Images (Case study: Zaribar and Chitgar Lakes) F. Naeimi Asl & M. Akhoondzadeh https://doi.org/10.61186/jgst.14.2.33
277. Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series S. Olen & B. Bookhagen https://doi.org/10.3390/rs10081272
278. “The 20 July 2021 Major Flood Event” in Greater Zhengzhou, China: A Case Study of Flooding Severity and Landscape Characteristics Y. Duan et al. https://doi.org/10.3390/land11111921
279. Fusion of Sentinel-1 data with Sentinel-2 products to overcome non-favourable atmospheric conditions for the delineation of inundation maps I. Manakos et al. https://doi.org/10.1080/22797254.2019.1596757
280. Surface Water Extent Extraction in Prairie Environments Using Sentinel-1 Image-Pair Coherence P. Chen & G. Gunn https://doi.org/10.3390/glacies2020006
281. Mapping future flood susceptibility in Greater Yangon, Myanmar using Google Earth Engine and machine learning M. Mone et al. https://doi.org/10.1016/j.susgeo.2026.100025
282. Flooding extent cartography with Landsat TM imagery and regularized kernel Fisher's discriminant analysis M. Volpi et al. https://doi.org/10.1016/j.cageo.2013.03.009
283. Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping S. Martinis et al. https://doi.org/10.1016/j.rse.2022.113077
284. Techniques of Remote Sensing and GIS as Tools for Visualizing Impact of Climate Change-Induced Flood in the Southern African Region Y. Twumasi et al. https://doi.org/10.4236/ajcc.2017.62016
285. Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon L. Moya et al. https://doi.org/10.3390/rs12142244
286. Improved integrated framework for flooded crop damage and recovery assessment: A multi-source earth observation and participatory mapping in Hadejia, Nigeria L. Lateef et al. https://doi.org/10.1016/j.jenvman.2025.125542
287. SAR and InSAR for Flood Monitoring: Examples With COSMO-SkyMed Data A. Refice et al. https://doi.org/10.1109/JSTARS.2014.2305165
288. Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe A. Tarpanelli et al. https://doi.org/10.5194/nhess-22-2473-2022
289. Sentinel-1-Based Water and Flood Mapping: Benchmarking Convolutional Neural Networks Against an Operational Rule-Based Processing Chain M. Bereczky et al. https://doi.org/10.1109/JSTARS.2022.3152127
290. Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data F. Cian et al. https://doi.org/10.1016/j.rse.2018.03.006
291. An Intercomparison of Sentinel-1 Based Change Detection Algorithms for Flood Mapping M. Tupas et al. https://doi.org/10.3390/rs15051200
292. DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping A. D'Addabbo et al. https://doi.org/10.1016/j.cageo.2017.12.005
293. Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources S. Saha et al. https://doi.org/10.1016/j.rsase.2022.100917
294. Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine J. Freihardt & O. Frey https://doi.org/10.5194/nhess-23-751-2023
295. Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors H. Mizuochi et al. https://doi.org/10.3390/rs10081235
296. Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery A. Phan et al. https://doi.org/10.3390/rs11172034
297. Monitoring surface water in floodplains by satellites: Progress, challenges, and perspectives Y. Lin & C. Song https://doi.org/10.1016/j.jhydrol.2025.134458
298. Time series analysis of automated surface water extraction and thermal pattern variation over the Betwa river, India N. Das et al. https://doi.org/10.1016/j.asr.2021.04.020
299. Increased flooded area and exposure in the White Volta river basin in Western Africa, identified from multi-source remote sensing data C. Li et al. https://doi.org/10.1038/s41598-022-07720-4
300. Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. https://doi.org/10.5194/hess-26-4345-2022
301. Enhanced flood water depth estimation from Sentinel-1A images A. Nguyen et al. https://doi.org/10.1080/01431161.2023.2268819
302. Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model I. Zotou et al. https://doi.org/10.3390/w14244020
303. Flood Detection in Urban Areas Using TerraSAR-X D. Mason et al. https://doi.org/10.1109/TGRS.2009.2029236
304. A Practical Plateau Lake Extraction Algorithm Combining Novel Statistical Features and Kullback–Leibler Distance Using Synthetic Aperture Radar Imagery X. Zhou et al. https://doi.org/10.1109/JSTARS.2020.3016349
305. Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery M. Butenuth et al. https://doi.org/10.1080/01431161.2010.542204