271 citations as recorded by crossref.

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2. Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. 10.3390/ijerph19010237
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8. 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 10.1007/s11356-019-06849-6
9. A methodology for mapping annual flood extent using multi-temporal Sentinel-1 imagery T. McCormack et al. 10.1016/j.rse.2022.113273
10. Estimation of Flood Inundation and Depth During Hurricane Florence Using Sentinel-1 and UAVSAR Data S. Kundu et al. 10.1109/LGRS.2022.3165444
11. Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan A. Tariq et al. 10.3390/f12101371
12. OBIA Flood Delimitation Assisted by Threshold Determination with Principal Component Analysis D. Roque et al. 10.14358/PERS.80.6.551-557
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15. Evaluating the impact of flood inundation with the cloud computing platform over vegetation cover of Ganga Basin during COVID-19 S. Ghosh et al. 10.1007/s41324-022-00430-z
16. Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine V. Tiwari et al. 10.1371/journal.pone.0237324
17. Earth observation data based rapid flood-extent modelling for tsunami-devastated coastal areas S. Hese & T. Heyer 10.1016/j.jag.2015.11.005
18. Brief communication: Hurricane Dorian: automated near-real-time mapping of the “unprecedented” flooding in the Bahamas using synthetic aperture radar D. Cerrai et al. 10.5194/nhess-20-1463-2020
19. The last two decades of computer vision technologies in water resource management: A bibliometric analysis U. Iqbal et al. 10.1111/wej.12845
20. The potential of Sentinel-1 imagery for flood event detection: A satellite vs. hydraulic model comparison J. Breznik et al. 10.1016/j.jhydrol.2024.132587
21. Flood mapping under vegetation using single SAR acquisitions S. Grimaldi et al. 10.1016/j.rse.2019.111582
22. Towards developing comparable optical and SAR remote sensing inundation mapping with hydrodynamic modelling C. Ticehurst & F. Karim 10.1080/01431161.2023.2211714
23. A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact S. Van Ackere et al. 10.3390/w11112275
24. Flood period estimation using multi-sensor satellite data: Case study on Punjab floods 2023 Y. Bhageerath et al. 10.1007/s12040-024-02499-6
25. A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet Y. Zhang et al. 10.1080/17538947.2023.2230978
26. The International Charter ‘Space and Major Disasters’: DLR’s Contributions to Emergency Response Worldwide S. Martinis et al. 10.1007/s41064-017-0032-1
27. Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery I. Hlaváčová et al. 10.3390/w13233392
28. Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP) P. Gautam et al. 10.1007/s12517-024-11910-x
29. Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing J. Kim et al. 10.3390/rs14246373
30. Microwave remote sensing of flood inundation G. Schumann & D. Moller 10.1016/j.pce.2015.05.002
31. Enhancing precision of flood estimation in EOS-04 SAR imagery: a statistical approach Y. Bhageerath et al. 10.1080/19475705.2024.2413910
32. Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study F. Carreño Conde & M. De Mata Muñoz 10.3390/w11122454
33. Automatic extraction of flood inundation areas from SAR images: a case study of Jilin, China during the 2017 flood disaster L. Wan et al. 10.1080/01431161.2019.1577999
34. A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery F. Bioresita et al. 10.3390/rs10020217
35. Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion Y. Li et al. 10.3390/rs11192231
36. Flood Inundation Distribution In Lamongan Regency Using Sentinel-1 SAR Data And Automatic Water-S1 Method L. Azizah et al. 10.1088/1755-1315/1418/1/012052
37. The use of remote sensing to characterise hydromorphological properties of European rivers S. Bizzi et al. 10.1007/s00027-015-0430-7
38. An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery J. Li & S. Wang 10.1080/01431161.2015.1009653
39. A large-scale 2005–2012 flood map record derived from ENVISAT-ASAR data: United Kingdom as a test case J. Zhao et al. 10.1016/j.rse.2021.112338
40. A fully automated TerraSAR-X based flood service S. Martinis et al. 10.1016/j.isprsjprs.2014.07.014
41. Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations E. Oliveira et al. 10.3390/rs11111305
42. Use of Sentinel-1 GRD SAR Images to Delineate Flood Extent in Pakistan M. Zhang et al. 10.3390/su12145784
43. On the Exploitation of Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions Q. Zhao et al. 10.3390/rs14102384
44. Detecting Flooded Areas Using Sentinel-1 SAR Imagery F. Alonso-Sarria et al. 10.3390/rs17081368
45. Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images C. Cazals et al. 10.3390/rs8070570
46. Operational Surface Water Detection and Monitoring Using Radarsat 2 S. Bolanos et al. 10.3390/rs8040285
47. Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands A. Hardy et al. 10.3390/rs12071182
48. A Framework to Assess Remote Sensing Algorithms for Satellite-Based Flood Index Insurance M. Thomas et al. 10.1109/JSTARS.2023.3244098
49. 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. 10.3390/rs6087819
50. 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 10.1016/j.jag.2019.102003
51. Flood Forecasting in Large River Basins Using FOSS Tool and HPC U. Dutta et al. 10.3390/w13243484
52. Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas S. Hu et al. 10.3390/rs12020243
53. Snow Avalanche Debris Analysis Using Time Series of Dual-Polarimetric Synthetic Aperture Radar Data S. Schlaffer & M. Schlögl 10.1109/JSTARS.2024.3423403
54. Probabilistic mapping of flood-induced backscatter changes in SAR time series S. Schlaffer et al. 10.1016/j.jag.2016.12.003
55. Extreme rainfall-induced urban flood monitoring and damage assessment in Wuhan (China) and Kumamoto (Japan) cities using Google Earth Engine A. Pandey et al. 10.1007/s10661-022-10076-x
56. Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping F. Bioresita et al. 10.1080/01431161.2019.1624869
57. Flood Extent Mapping in the Caprivi Floodplain Using Sentinel-1 Time Series T. Bangira et al. 10.1109/JSTARS.2021.3083517
58. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery J. Liang & D. Liu 10.1016/j.isprsjprs.2019.10.017
59. Where is my attention? An explainable AI exploration in water detection from SAR imagery L. Chen et al. 10.1016/j.jag.2024.103878
60. Flood Mapping With TerraSAR-X in Forested Regions in Estonia K. Voormansik et al. 10.1109/JSTARS.2013.2283340
61. Brief communication: Key papers of 20 years in Natural Hazards and Earth System Sciences A. Gain et al. 10.5194/nhess-22-985-2022
62. Integration of SAR and multi-spectral imagery in flood inundation mapping – a case study on Kerala floods 2018 J. Jacinth Jennifer et al. 10.1080/09715010.2020.1791265
63. On Flood Detection Using Dual-Polarimetric SAR Observation S. Kim et al. 10.3390/rs17111931
64. Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies F. Xue et al. 10.1109/JSTARS.2022.3176388
65. A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data E. Pedzisai et al. 10.1016/j.heliyon.2023.e13332
66. A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness J. Eudaric et al. 10.1038/s41598-024-69977-1
67. Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion M. Ouled Sghaier et al. 10.3390/rs10020237
68. The Synergistic Use of RADARSAT-2 Ascending and Descending Images to Improve Surface Water Detection Accuracy in Alberta, Canada E. DeLancey et al. 10.1080/07038992.2019.1691516
69. Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning X. Jiang et al. 10.1016/j.isprsjprs.2021.05.019
70. Flood Hazard Mapping Combining Hydrodynamic Modeling and Multi Annual Remote Sensing data L. Giustarini et al. 10.3390/rs71014200
71. 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 10.5194/nhess-18-1905-2018
72. Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas H. Cao et al. 10.3390/w11040786
73. Inundation Extent Mapping by Synthetic Aperture Radar: A Review X. Shen et al. 10.3390/rs11070879
74. A review of remote sensing applications for water security: Quantity, quality, and extremes I. Chawla et al. 10.1016/j.jhydrol.2020.124826
75. Discrimination of Water Surfaces, Heavy Rainfall, and Wet Snow Using COSMO-SkyMed Observations of Severe Weather Events L. Pulvirenti et al. 10.1109/TGRS.2013.2244606
76. Sentetik Açıklıklı Radar verilerinin Taşkın Çalışmalarında Kullanılması: Berdan Ovası Taşkını M. AKGÜL 10.29128/geomatik.378123
77. Inland Surface Waters Quantity Monitored from Remote Sensing J. Cretaux et al. 10.1007/s10712-023-09803-x
78. Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data K. Brown et al. 10.1177/0309133316633570
79. Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning V. Katiyar et al. 10.3390/rs13122334
80. 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. 10.3390/rs15102690
81. Towards Operational Flood Monitoring in Flanders Using Sentinel-1 L. Landuyt et al. 10.1109/JSTARS.2021.3121992
82. 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. 10.1080/19475705.2023.2202296
83. A Novel Fully Automated Mapping of the Flood Extent on SAR Images Using a Supervised Classifier A. Benoudjit & R. Guida 10.3390/rs11070779
84. Identifying the effect of monsoon floods on vegetation and land surface temperature by using Google Earth Engine S. Rahaman & N. Shermin 10.1016/j.uclim.2022.101162
85. Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation L. Pulvirenti et al. 10.1016/j.rse.2010.12.002
86. Observing floods from space: Experience gained from COSMO-SkyMed observations N. Pierdicca et al. 10.1016/j.actaastro.2012.10.034
87. Flood depth estimation by means of high-resolution SAR images and lidar data F. Cian et al. 10.5194/nhess-18-3063-2018
88. Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case of the 2015 Heavy Rainfall in Japan M. Ohki et al. 10.1109/JSTARS.2019.2911596
89. 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. 10.3390/rs12010195
90. 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. 10.5194/hess-26-841-2022
91. RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1 G. Oakes et al. 10.3390/rs15061705
92. 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. 10.1002/hyp.8268
93. Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery E. Nemni et al. 10.3390/rs12162532
94. Flooding Water Depth Estimation With High-Resolution SAR P. Iervolino et al. 10.1109/TGRS.2014.2358501
95. 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. 10.1016/j.rse.2021.112837
96. Extraction and Classification of Flood-Affected Areas Based on MRF and Deep Learning J. Wang et al. 10.3390/w15071288
97. Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part I: Comparison and Analysis Y. Xia et al. 10.1175/JHM-D-14-0096.1
98. Hydrologic regime dynamics and driving factors of Anglaren Co on the Tibetan Plateau based on multi-source remote sensing data D. Huang et al. 10.1016/j.jhydrol.2025.133645
99. Automatic flood detection using sentinel-1 images on the google earth engine M. Moharrami et al. 10.1007/s10661-021-09037-7
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101. Flood Inundation Probability Estimation by Integrating Physical and Social Sensing Data: Case Study of 2021 Heavy Rainfall in Henan, China W. Du et al. 10.3390/rs16152734
102. Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java, Indonesia F. Yulianto et al. 10.1007/s11069-015-1633-x
103. Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series C. Kuenzer et al. 10.3390/rs70708516
104. Assimilating SAR-derived water level data into a hydraulic model: a case study L. Giustarini et al. 10.5194/hess-15-2349-2011
105. L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates Z. Kugler et al. 10.3390/rs11070835
106. Satellite Imagery-Based Identification of High-Risk Areas of Schistosome Intermediate Snail Hosts Spread after Flood J. Qiu et al. 10.3390/rs14153707
107. Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm S. Yang et al. 10.1016/j.scitotenv.2024.174329
108. Mapping Flooded Vegetation Using COSMO-SkyMed: Comparison With Polarimetric and Optical Data Over Rice Fields N. Pierdicca et al. 10.1109/JSTARS.2017.2711960
109. An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data L. Pulvirenti et al. 10.3390/rs12040674
110. Flood Damage Assessment Through Multitemporal COSMO-SkyMed Data and Hydrodynamic Models: The Albania 2010 Case Study L. Pulvirenti et al. 10.1109/JSTARS.2014.2328012
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113. Deep Learning Ensemble for Flood Probability Analysis F. Sseguya & K. Jun 10.3390/w16213092
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115. River flood mapping in urban areas combining Radarsat-2 data and flood return period data M. Tanguy et al. 10.1016/j.rse.2017.06.042
116. 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. 10.3390/rs16122078
117. The Use of C-Band and X-Band SAR with Machine Learning for Detecting Small-Scale Mining G. Janse van Rensburg & J. Kemp 10.3390/rs14040977
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125. Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images D. Amitrano et al. 10.1109/TGRS.2018.2797536
126. The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Hara et al. 10.2478/ijafr-2019-0006
127. Optimized Rule-Based Flood Mapping Technique Using Multitemporal RADARSAT-2 Images in the Tropical Region B. Pradhan et al. 10.1109/JSTARS.2017.2676343
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129. Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data F. Yulianto et al. 10.1155/2022/4894929
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132. Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence L. Pulvirenti et al. 10.1109/TGRS.2015.2482001
133. Evaluation of a new 18-year MODIS-derived surface water fraction dataset for constructing Mediterranean wetland open surface water dynamics L. Li et al. 10.1016/j.jhydrol.2020.124956
134. Development of an Automated Tool for Delineation of Flood Footprints from SAR Imagery for Rapid Disaster Response: A Case Study S. Kuntla & P. Manjusree 10.1007/s12524-020-01125-4
135. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston F. Brill et al. 10.3390/rs13112042
136. Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case M. Chini et al. 10.3390/rs11020107
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143. Flood extent mapping for Namibia using change detection and thresholding with SAR S. Long et al. 10.1088/1748-9326/9/3/035002
144. Automated Extraction of Inundated Areas from Multi-Temporal Dual-Polarization RADARSAT-2 Images of the 2011 Central Thailand Flood P. Nakmuenwai et al. 10.3390/rs9010078
145. Evaluation of Sentinel-1 data for flood mapping in the upstream of Sidi Salem dam (Northern Tunisia) A. Ezzine et al. 10.1007/s12517-018-3505-7
146. Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images W. Liu et al. 10.3390/rs13040639
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149. Utilization of Satellite-based Digital Elevation Model (DEM) for Hydrologic Applications: A Review S. Zaidi et al. 10.1007/s12594-018-1016-5
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153. 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. 10.3390/rs12193218
154. Extending general compact querieable representations to GIS applications N. Brisaboa et al. 10.1016/j.ins.2019.08.007
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160. Implications of boreal forest stand characteristics for X-band SAR flood mapping accuracy J. Cohen et al. 10.1016/j.rse.2016.08.016
161. 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. 10.1080/24749508.2023.2187567
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172. Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats A. Hardy et al. 10.3390/rs11050593
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