192 citations as recorded by crossref.

1. Multi-hazard vulnerability zone identification using GIS-based fuzzy AHP and MCDM techniques A. Sood et al. https://doi.org/10.1007/s11069-025-07125-2
2. Do Remote Sensing Mapping Practices Adequately Address Localized Flooding? A Critical Overview R. Malinowski et al. https://doi.org/10.1007/s40362-017-0043-8
3. Operational SAR flood mapping as a full-stack systems problem: an AI-enabled perspective Q. Yang https://doi.org/10.3389/frwa.2026.1871753
4. 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
5. Swarm‐based optimizer for convolutional neural network: An application for flood susceptibility mapping T. Chou et al. https://doi.org/10.1111/tgis.12715
6. 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
7. 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
8. The need for scientific rigour and accountability in flood mapping to better support disaster response G. Schumann https://doi.org/10.1002/hyp.13547
9. 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
10. Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference M. Tupas et al. https://doi.org/10.3390/w15234034
11. DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection Z. Wang et al. https://doi.org/10.1109/JSTARS.2025.3584282
12. 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
13. Fusion of SAR and Multi-spectral Time Series for Determination of Water Table Depth and Lake Area in Peatlands K. Krzepek et al. https://doi.org/10.1007/s41064-022-00216-w
14. GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia D. Souissi et al. https://doi.org/10.1080/10106049.2019.1566405
15. 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
16. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods D. Bui et al. https://doi.org/10.1016/j.scitotenv.2019.02.422
17. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston F. Brill et al. https://doi.org/10.3390/rs13112042
18. Flood Detection/Monitoring Using Adjustable Histogram Equalization Technique F. Nazir et al. https://doi.org/10.1155/2014/809636
19. 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
20. The 2011 Tohoku (Japan) Tsunami Inundation and Liquefaction Investigated Through Optical, Thermal, and SAR Data M. Chini et al. https://doi.org/10.1109/LGRS.2012.2205661
21. Rapid Probabilistic Inundation Mapping Using Local Thresholds and Sentinel-1 SAR Data on Google Earth Engine J. Liang et al. https://doi.org/10.3390/rs17101747
22. 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
23. 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
24. Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery R. Amer https://doi.org/10.3390/rs17111869
25. Assessing flood susceptibility in Hanoi using machine learning and remote sensing: implications for urban health and resilience T. Van Pham et al. https://doi.org/10.1007/s11069-025-07211-5
26. Advanced remote sensing techniques in flash flood delineation in Tabuk City, Saudi Arabia M. Elhag & S. Abdurahman https://doi.org/10.1007/s11069-020-04135-0
27. Blending MODIS and Landsat images for urban flood mapping F. Zhang et al. https://doi.org/10.1080/01431161.2014.903351
28. 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
29. 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
30. 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
31. 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
32. Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets A. Melancon et al. https://doi.org/10.3390/rs13245098
33. Mapping Flood Extent and Frequency from Sentinel-1 Imagery during the Extremely Warm Winter of 2020 in Boreal Floodplains and Forests L. Sipelgas et al. https://doi.org/10.3390/rs13234949
34. Flood hazard assessment and the role of citizen science B. Sy et al. https://doi.org/10.1111/jfr3.12519
35. BiEHFFNet: A Water Body Detection Network for SAR Images Based on Bi-Encoder and Hybrid Feature Fusion B. Han et al. https://doi.org/10.3390/math13152347
36. Enhancing Landslide Vulnerability Mapping Through Automated Fuzzy Logic Algorithm-Based Methodology A. Bruzón et al. https://doi.org/10.1007/s10706-023-02714-z
37. 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
38. Comparative Analysis among Radar Image Filters for Flood Mapping D. Kim et al. https://doi.org/10.7848/ksgpc.2016.34.1.43
39. An assessment of metaheuristic approaches for flood assessment H. Pourghasemi et al. https://doi.org/10.1016/j.jhydrol.2019.124536
40. Exploiting the proliferation of current and future satellite observations of rivers G. Schumann & A. Domeneghetti https://doi.org/10.1002/hyp.10825
41. A Collection of SAR Methodologies for Monitoring Wetlands L. White et al. https://doi.org/10.3390/rs70607615
42. Unsupervised Continuous Flood Mapping From Multitemporal, Multiview, and Multisensor SAR Sequences S. Miakhil et al. https://doi.org/10.1109/JSTARS.2026.3707063
43. GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le–Kien Giang watershed, Vietnam H. Nguyen https://doi.org/10.1007/s12145-022-00825-4
44. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS D. Tien Bui et al. https://doi.org/10.1016/j.jhydrol.2016.06.027
45. 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
46. Flood mapping through principal component analysis of multitemporal satellite imagery considering the alteration of water spectral properties due to turbidity conditions D. Gómez-Palacios et al. https://doi.org/10.1080/19475705.2016.1250115
47. Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube B. Bauer-Marschallinger et al. https://doi.org/10.3390/rs14153673
48. Analysis and Interpretation of the COSMO-SkyMed Observations of the 2011 Japan Tsunami M. Chini et al. https://doi.org/10.1109/LGRS.2011.2182495
49. 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
50. MMFlood: A Multimodal Dataset for Flood Delineation From Satellite Imagery F. Montello et al. https://doi.org/10.1109/ACCESS.2022.3205419
51. Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification F. Foroughnia et al. https://doi.org/10.3390/rs14153718
52. Assessing and mapping multi-hazard risk susceptibility using a machine learning technique H. Pourghasemi et al. https://doi.org/10.1038/s41598-020-60191-3
53. 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
54. Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river B. Nghia et al. https://doi.org/10.1016/j.pdisas.2022.100235
55. SAR based flood risk analysis: A case study Kerala flood 2018 N. Pramanick et al. https://doi.org/10.1016/j.asr.2021.07.003
56. Modeling Wetland Habitat Quality in the Rarh Tract of Eastern India R. Khatun & S. Das https://doi.org/10.1007/s13157-024-01849-w
57. Sentetik Açıklıklı Radar verilerinin Taşkın Çalışmalarında Kullanılması: Berdan Ovası Taşkını M. AKGÜL https://doi.org/10.29128/geomatik.378123
58. Fusing Landsat-8, Sentinel-1, and Sentinel-2 Data for River Water Mapping Using Multidimensional Weighted Fusion Method Q. Liu et al. https://doi.org/10.1109/TGRS.2022.3187154
59. Flood Distance Algorithms and Fault Hidden Danger Recognition for Transmission Line Towers Based on SAR Images L. Liu et al. https://doi.org/10.3390/rs11141642
60. Flood Mapping from Satellite Imagery: A Response-Based Framework for Quantifying Flood Hazard and Uncertainty R. Ahmed et al. https://doi.org/10.1007/s41748-025-00862-1
61. Flood Hazard Mapping Combining Hydrodynamic Modeling and Multi Annual Remote Sensing data L. Giustarini et al. https://doi.org/10.3390/rs71014200
62. Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations E. Oliveira et al. https://doi.org/10.3390/rs11111305
63. Characterization and assessment of flood inundated areas of lower Brahmaputra River Basin using multitemporal Synthetic Aperture Radar data: A case study from NE India R. Devrani et al. https://doi.org/10.1002/gj.4365
64. On Flood Detection Using Dual-Polarimetric SAR Observation S. Kim et al. https://doi.org/10.3390/rs17111931
65. Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images Z. Wang et al. https://doi.org/10.3390/rs14143473
66. 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
67. Real Time Estimation of the Calgary Floods Using Limited Remote Sensing Data E. Schnebele et al. https://doi.org/10.3390/w6020381
68. GIS-Based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) Techniques to Derive Flood Risks Management on Rice Productivity in Gishari Marshland J. Nsengiyumva et al. https://doi.org/10.4236/gep.2024.123013
69. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China H. Hong et al. https://doi.org/10.1016/j.scitotenv.2017.12.256
70. 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
71. Grand Challenges in Microwave Remote Sensing G. Schumann https://doi.org/10.3389/frsen.2020.603650
72. 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
73. Flood mapping using Sentinel-1 imagery with topographical and hydrological contextualization: Case study from Ribe, Denmark M. Hansen et al. https://doi.org/10.1016/j.jag.2025.104816
74. 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
75. Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method B. Tavus et al. https://doi.org/10.1016/j.scitotenv.2021.151585
76. High-Performance Segmentation for Flood Mapping of HISEA-1 SAR Remote Sensing Images S. Lv et al. https://doi.org/10.3390/rs14215504
77. Application of the GIS-Based Probabilistic Models for Mapping the Flood Susceptibility in Bansloi Sub-basin of Ganga-Bhagirathi River and Their Comparison G. Paul et al. https://doi.org/10.1007/s41976-019-00018-6
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. Use of Sentinel-1 GRD SAR Images to Delineate Flood Extent in Pakistan M. Zhang et al. https://doi.org/10.3390/su12145784
85. Towards Operational Flood Monitoring in Flanders Using Sentinel-1 L. Landuyt et al. https://doi.org/10.1109/JSTARS.2021.3121992
86. Inland Surface Waters Quantity Monitored from Remote Sensing J. Cretaux et al. https://doi.org/10.1007/s10712-023-09803-x
87. Flood mapping under vegetation using single SAR acquisitions S. Grimaldi et al. https://doi.org/10.1016/j.rse.2019.111582
88. 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
89. 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
90. Sentinel-1-based flood mapping: a fully automated processing chain A. Twele et al. https://doi.org/10.1080/01431161.2016.1192304
91. Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data H. Guan et al. https://doi.org/10.1016/j.rse.2023.113714
92. Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS H. Nguyen et al. https://doi.org/10.1007/s11356-024-32163-x
93. 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
94. Microwave remote sensing of flood inundation G. Schumann & D. Moller https://doi.org/10.1016/j.pce.2015.05.002
95. 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
96. Unlocking the full potential of Earth observation during the 2015 Texas flood disaster G. Schumann et al. https://doi.org/10.1002/2015WR018428
97. Integrating remote sensing and machine learning for flood modelling: A systematic literature review N. Manyaka et al. https://doi.org/10.1016/j.pce.2026.104315
98. 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
99. Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam H. Nguyen https://doi.org/10.2166/wcc.2022.257
100. Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data M. Rättich et al. https://doi.org/10.3390/rs12040643
101. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. https://doi.org/10.3390/rs16040656
102. Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood G. Ireland et al. https://doi.org/10.3390/rs70303372
103. Chamoli flash-flood mapping and evaluation with a supervised classifier and NDWI thresholding using Sentinel-2 optical data in Google earth engine S. Singh & M. Kansal https://doi.org/10.1007/s12145-022-00786-8
104. 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
105. 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
106. A fully automated TerraSAR-X based flood service S. Martinis et al. https://doi.org/10.1016/j.isprsjprs.2014.07.014
107. 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
108. 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
109. 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
110. Water Body Automated Extraction in Polarization SAR Images With Dense-Coordinate-Feature-Concatenate Network W. Xue et al. https://doi.org/10.1109/JSTARS.2021.3129182
111. 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
112. 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
113. Geoinformation Technologies in Support of Environmental Hazards Monitoring under Climate Change: An Extensive Review A. Tsatsaris et al. https://doi.org/10.3390/ijgi10020094
114. 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
115. Novel ensemble machine learning models in flood susceptibility mapping P. Prasad et al. https://doi.org/10.1080/10106049.2021.1892209
116. Detection and Delineation of Localized Flooding from WorldView-2 Multispectral Data R. Malinowski et al. https://doi.org/10.3390/rs71114853
117. 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
118. Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin T. Perrou et al. https://doi.org/10.1007/s11707-018-0711-2
119. Brief Communication: Contrast-stretching- and histogram-smoothness-based synthetic aperture radar image enhancement for flood map generation F. Nazir et al. https://doi.org/10.5194/nhess-15-273-2015
120. Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data E. Pontoglio et al. https://doi.org/10.3390/rs13193983
121. Impact of extreme weather events on cropland inundation over Indian subcontinent A. Prakash et al. https://doi.org/10.1007/s10661-022-10553-3
122. A Robust Deep Learning Ensemble Framework for Waterbody Detection Using High-Resolution X-Band SAR Under Data-Constrained Conditions S. Choi et al. https://doi.org/10.3390/rs18020301
123. 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
124. Radar System . Prof. Sugre D. D. et al. https://doi.org/10.48175/IJARSCT-24622
125. 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
126. Comparison between Different Distributed Methods for Flood Susceptibility Mapping L. Liuzzo et al. https://doi.org/10.1007/s11269-019-02293-w
127. Earth Observation Data Synergy for the Enhanced Monitoring of Ephemeral Water Bodies to Anticipate Karst-Related Flooding E. Papageorgiou et al. https://doi.org/10.3390/geohazards4020012
128. Spatiotemporal variation of water level in wetlands based on multi-source remote sensing data and responses to changing environments D. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.177060
129. Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery I. Hlaváčová et al. https://doi.org/10.3390/w13233392
130. A global network for operational flood risk reduction L. Alfieri et al. https://doi.org/10.1016/j.envsci.2018.03.014
131. Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm Y. Wang et al. https://doi.org/10.1016/j.jenvman.2019.06.102
132. Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data V. Tsyganskaya et al. https://doi.org/10.3390/rs10081286
133. Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery E. Nemni et al. https://doi.org/10.3390/rs12162532
134. Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia A. Rimba et al. https://doi.org/10.3390/w15213783
135. OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion G. Drakonakis et al. https://doi.org/10.1109/JSTARS.2022.3155559
136. Pakistan's 2022 floods: Spatial distribution, causes and future trends from Sentinel-1 SAR observations F. Chen et al. https://doi.org/10.1016/j.rse.2024.114055
137. Assessing flood inundation mapping through estimated discharge using GIS and HEC-RAS model E. Mokhtar et al. https://doi.org/10.1007/s12517-018-4040-2
138. Analysing sensitivity of conditioning factors for wetland habitat quality modelling R. Khatun & S. Das https://doi.org/10.1016/j.ecohyd.2024.03.008
139. 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
140. A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data B. Hu et al. https://doi.org/10.5194/hess-29-6023-2025
141. Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas C. Cao et al. https://doi.org/10.3390/su8090948
142. SAR image analysis techniques for flood area mapping - literature survey R. Manavalan https://doi.org/10.1007/s12145-016-0274-2
143. 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
144. Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches A. Dasgupta et al. https://doi.org/10.1016/j.rse.2018.06.019
145. Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models S. Mahato et al. https://doi.org/10.1016/j.gsf.2021.101175
146. SAR-based detection of flooded vegetation – a review of characteristics and approaches V. Tsyganskaya et al. https://doi.org/10.1080/01431161.2017.1420938
147. Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation A. Shastry et al. https://doi.org/10.1016/j.rse.2023.113556
148. Flood Risk Assessment in the Lower Valley of Ouémé, Benin Y. Bossa et al. https://doi.org/10.4236/ojmh.2024.142008
149. 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
150. Inundation Extent Mapping by Synthetic Aperture Radar: A Review X. Shen et al. https://doi.org/10.3390/rs11070879
151. Synergistic Use of Geospatial Data for Water Body Extraction from Sentinel-1 Images for Operational Flood Monitoring across Southeast Asia Using Deep Neural Networks J. Kim et al. https://doi.org/10.3390/rs13234759
152. Water depth estimate and flood extent enhancement for satellite-based inundation maps A. Betterle & P. Salamon https://doi.org/10.5194/nhess-24-2817-2024
153. 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
154. Observing floods from space: Experience gained from COSMO-SkyMed observations N. Pierdicca et al. https://doi.org/10.1016/j.actaastro.2012.10.034
155. 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
156. The Contribution of SIASGE Radar Data Integrated With Optical Images to Support Thematic Mapping at Regional Scale N. Pierdicca et al. https://doi.org/10.1109/JSTARS.2014.2330744
157. 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
158. Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features V. Tsyganskaya et al. https://doi.org/10.3390/w11091938
159. Flood Extent Mapping From SAR Images by a Dynamic Threshold: A Case Study of 2022 Compound Flood in Northeastern Bangladesh R. Ahmed et al. https://doi.org/10.1109/JSTARS.2025.3647843
160. 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
161. GIS-based statistical model for the prediction of flood hazard susceptibility S. Malik et al. https://doi.org/10.1007/s10668-021-01377-1
162. Mapping of Spatio-Temporal Changes of Surface Water Using Sentinel-1 SAR Images L. Kseňak et al. https://doi.org/10.1088/1755-1315/906/1/012059
163. Integrating MCDA and Rain-on-Grid Modeling for Flood Hazard Mapping in Bahrah City, Saudi Arabia A. Hidayatulloh et al. https://doi.org/10.3390/geosciences16010032
164. Multiscale Feature-Enhanced Water Body Detector of Truncated Gaussian Clutter in SAR Imagery B. Zhu et al. https://doi.org/10.1109/JSTARS.2024.3522997
165. 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
166. Effectiveness of aerial and ISERV-ISS RGB photos for real-time urban floodwater mapping: case of Calgary 2013 flood Y. Zhang et al. https://doi.org/10.1117/1.JRS.13.044521
167. Geospatial analysis of South Lhonak Lake outburst triggered on October, 2023 P. Biswakarma et al. https://doi.org/10.1007/s43538-025-00499-8
168. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution H. Hong et al. https://doi.org/10.1016/j.scitotenv.2017.10.114
169. UAV-Based Survey of the Earth Pyramids at the Kuklica Geosite (North Macedonia) I. Milevski et al. https://doi.org/10.3390/heritage8010006
170. Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania M. Popa et al. https://doi.org/10.3390/w11102116
171. Identifying Spatially Correlated Patterns between Surface Water and Frost Risk Using EO Data and Geospatial Indices P. Louka et al. https://doi.org/10.3390/w12030700
172. Near-real-time SAR flood detection using feature fusion and region-adaptive deep learning method S. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135523
173. Enhancing Detection of Inundated Areas Using Novel Hybrid PolSAR-Metaheuristic-Deep Learning Models S. Lee et al. https://doi.org/10.1109/JSTARS.2026.3704360
174. 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
175. Probabilistic Mapping of August 2018 Flood of Kerala, India, Using Space-Borne Synthetic Aperture Radar S. Sherpa et al. https://doi.org/10.1109/JSTARS.2020.2970337
176. Spatiotemporal change of lake shrinkage and its response to the unprecedented heat waves in the middle and lower Yangtze River Basin J. Wang et al. https://doi.org/10.1080/01431161.2024.2387132
177. Study of Islands Mapping Using High Resolution Satellite SAR Data 栋. 姜 https://doi.org/10.12677/GST.2019.72009
178. 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
179. Towards Sustainable Land Uses within the Elbe River Biosphere Reserve in Lower Saxony, Germany by Means of TerraSAR-X Images D. Farghaly et al. https://doi.org/10.4236/gep.2016.43009
180. Automated SAR Image Thresholds for Water Mask Production in Alberta’s Boreal Region C. Mahoney et al. https://doi.org/10.3390/rs12142223
181. STUDY ON ESTIMATION METHOD OF INUNDATION AREA AND DEPTH USING SAR DATA H. SHIROZU et al. https://doi.org/10.2208/jscejhe.75.2_I_1471
182. SAR and InSAR for Flood Monitoring: Examples With COSMO-SkyMed Data A. Refice et al. https://doi.org/10.1109/JSTARS.2014.2305165
183. Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew Y. Lin et al. https://doi.org/10.3390/rs11151778
184. Analysis and Processing of the COSMO-SkyMed Second Generation Images of the 2022 Marche (Central Italy) Flood L. Pulvirenti et al. https://doi.org/10.3390/w15071353
185. Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques M. Bordbar et al. https://doi.org/10.1038/s41598-022-05364-y
186. Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment S. Scarpino et al. https://doi.org/10.3390/ijgi7030105
187. Enhancement of Urban Floodwater Mapping From Aerial Imagery With Dense Shadows via Semisupervised Learning Y. He et al. https://doi.org/10.1109/JSTARS.2022.3215730
188. A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods D. Tien Bui & N. Hoang https://doi.org/10.5194/gmd-10-3391-2017
189. 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
190. 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
191. Advanced modeling for flash flood susceptibility mapping using remote sensing and GIS techniques: a case study in Northeast Algeria A. Mansour et al. https://doi.org/10.1007/s12665-023-11324-0
192. Monitoring monthly surface water dynamics of Dongting Lake using Sentinal-1 data at 10 m L. Xing et al. https://doi.org/10.7717/peerj.4992