113 citations as recorded by crossref.

1. Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery C. Huang et al. 10.1016/j.jag.2013.09.002
2. Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling K. Markert et al. 10.3390/rs16071262
3. A Novel Fully Automated Mapping of the Flood Extent on SAR Images Using a Supervised Classifier A. Benoudjit & R. Guida 10.3390/rs11070779
4. Development and evaluation of a framework for global flood hazard mapping F. Dottori et al. 10.1016/j.advwatres.2016.05.002
5. Time series analysis of automated surface water extraction and thermal pattern variation over the Betwa river, India N. Das et al. 10.1016/j.asr.2021.04.020
6. Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas H. Cao et al. 10.3390/w11040786
7. Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images D. Amitrano et al. 10.1109/TGRS.2018.2797536
8. On the Use of Global Flood Forecasts and Satellite-Derived Inundation Maps for Flood Monitoring in Data-Sparse Regions B. Revilla-Romero et al. 10.3390/rs71115702
9. Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon L. Moya et al. 10.3390/rs12142244
10. Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy N. Ivan Ulloa et al. 10.3390/rs12091384
11. RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1 G. Oakes et al. 10.3390/rs15061705
12. Seasonal cycles of lakes on the Tibetan Plateau detected by Sentinel-1 SAR data Y. Zhang et al. 10.1016/j.scitotenv.2019.135563
13. Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies F. Xue et al. 10.1109/JSTARS.2022.3176388
14. UNet Combined With Attention Mechanism Method for Extracting Flood Submerged Range W. Li et al. 10.1109/JSTARS.2022.3194375
15. Probabilistic River Water Mapping from Landsat-8 Using the Support Vector Machine Method Q. Liu et al. 10.3390/rs12091374
16. Fusing Landsat-8, Sentinel-1, and Sentinel-2 Data for River Water Mapping Using Multidimensional Weighted Fusion Method Q. Liu et al. 10.1109/TGRS.2022.3187154
17. A fully automated TerraSAR-X based flood service S. Martinis et al. 10.1016/j.isprsjprs.2014.07.014
18. Surface water detection and delineation using remote sensing images: a review of methods and algorithms T. Bijeesh & K. Narasimhamurthy 10.1007/s40899-020-00425-4
19. Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany S. Martinis & C. Rieke 10.3390/rs70607732
20. DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping A. D'Addabbo et al. 10.1016/j.cageo.2017.12.005
21. A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR) E. Park et al. 10.3390/rs12081353
22. 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
23. Object-Based Flood Analysis Using a Graph-Based Representation B. Debusscher & F. Van Coillie 10.3390/rs11161883
24. Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping J. Zhao et al. 10.1016/j.rse.2021.112668
25. Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification E. Goumehei et al. 10.1029/2019WR025192
26. Exploiting the proliferation of current and future satellite observations of rivers G. Schumann & A. Domeneghetti 10.1002/hyp.10825
27. A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case M. Chini et al. 10.1109/TGRS.2017.2737664
28. An Intercomparison of Sentinel-1 Based Change Detection Algorithms for Flood Mapping M. Tupas et al. 10.3390/rs15051200
29. River Delineation from Remotely Sensed Imagery Using a Multi-Scale Classification Approach K. Yang et al. 10.1109/JSTARS.2014.2309707
30. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges S. Grimaldi et al. 10.1007/s10712-016-9378-y
31. The Pakistan Flood of August 2022: Causes and Implications J. Nanditha et al. 10.1029/2022EF003230
32. Unlocking the full potential of Earth observation during the 2015 Texas flood disaster G. Schumann et al. 10.1002/2015WR018428
33. Repeated Hurricanes Reveal Risks and Opportunities for Social-Ecological Resilience to Flooding and Water Quality Problems D. Schaffer-Smith et al. 10.1021/acs.est.9b07815
34. Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine A. Gulácsi & F. Kovács 10.3390/rs12101614
35. Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: A hydro-geomorphic approach K. Mishra & R. Sinha 10.1016/j.geomorph.2019.106861
36. Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model I. Zotou et al. 10.3390/w14244020
37. Sentinel-1-Based Water and Flood Mapping: Benchmarking Convolutional Neural Networks Against an Operational Rule-Based Processing Chain M. Bereczky et al. 10.1109/JSTARS.2022.3152127
38. Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference M. Tupas et al. 10.3390/w15234034
39. Surface water maps de-noising and missing-data filling using determinist spatial filters based on several a priori information F. Aires 10.1016/j.rse.2019.111481
40. Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data E. Pontoglio et al. 10.3390/rs13193983
41. A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data C. Huang et al. 10.3390/ijgi6050140
42. Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images W. Liu et al. 10.3390/rs13040639
43. Pakistan's 2022 floods: Spatial distribution, causes and future trends from Sentinel-1 SAR observations F. Chen et al. 10.1016/j.rse.2024.114055
44. GloFAS – global ensemble streamflow forecasting and flood early warning L. Alfieri et al. 10.5194/hess-17-1161-2013
45. Probabilistic Flood Mapping Using Synthetic Aperture Radar Data L. Giustarini et al. 10.1109/TGRS.2016.2592951
46. A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka N. Alahacoon et al. 10.3390/rs10030448
47. 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
48. 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
49. Utilising Sentinel-1’s orbital stability for efficient pre-processing of sigma nought backscatter C. Navacchi et al. 10.1016/j.isprsjprs.2022.07.023
50. Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube B. Bauer-Marschallinger et al. 10.3390/rs14153673
51. Sentinel-1-based flood mapping: a fully automated processing chain A. Twele et al. 10.1080/01431161.2016.1192304
52. The Global Water Body Layer from TanDEM-X Interferometric SAR Data J. Bueso-Bello et al. 10.3390/rs13245069
53. Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps D. Mason et al. 10.3390/w13111577
54. Weekly mapping of surface water extent in the intertropical wetlands using spaceborne GNSS reflectometry P. Zeiger et al. 10.1016/j.jhydrol.2023.130305
55. Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine K. Markert et al. 10.3390/rs12152469
56. Limitations Posed by Free DEMs in Watershed Studies: The Case of River Tanaro in Italy R. Tavares da Costa et al. 10.3389/feart.2019.00141
57. Comparison of visible and multi-satellite global inundation datasets at high-spatial resolution F. Aires et al. 10.1016/j.rse.2018.06.015
58. Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania M. Popa et al. 10.3390/w11102116
59. Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition Q. Song et al. 10.3390/rs15194655
60. Hazard Assessments of Riverbank Flooding and Backward Flows in Dike-Through Drainage Ditches during Moderate Frequent Flooding Events in the Ningxia Reach of the Upper Yellow River (NRYR) F. Tian et al. 10.3390/w11071477
61. Application of Gated Recurrent Unit Neural Network for Flood Extraction from Synthetic Aperture Radar Time Series M. Zhang et al. 10.3390/w15213779
62. A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level K. Yan et al. 10.1002/hyp.10449
63. 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
64. Potential of SAR-Derived Flood Maps for Hydrodynamic Model Calibration in Data Scarce Regions A. Dasgupta et al. 10.1061/(ASCE)HE.1943-5584.0001988
65. Monitoring of the Spatio-Temporal Dynamics of the Floods in the Guayas Watershed (Ecuadorian Pacific Coast) Using Global Monitoring ENVISAT ASAR Images and Rainfall Data F. Frappart et al. 10.3390/w9010012
66. A MODIS-based automated flood monitoring system for southeast asia A. Ahamed & J. Bolten 10.1016/j.jag.2017.05.006
67. An Efficient Global Scale Sentinel-1 Radar Backscatter and Interferometric Processing System P. Agram et al. 10.3390/rs14153524
68. Automated Extraction of Surface Water Extent from Sentinel-1 Data W. Huang et al. 10.3390/rs10050797
69. Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data R. Sharma et al. 10.3390/rs71013807
70. Advances in pan‐European flood hazard mapping L. Alfieri et al. 10.1002/hyp.9947
71. Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems? Y. Zhang et al. 10.1175/JHM-D-14-0048.1
72. A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery F. Bioresita et al. 10.3390/rs10020217
73. A Collection of SAR Methodologies for Monitoring Wetlands L. White et al. 10.3390/rs70607615
74. Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review P. Sogno et al. 10.3390/rs14102475
75. Challenges in Complementing Data from Ground-Based Sensors with Satellite-Derived Products to Measure Ecological Changes in Relation to Climate—Lessons from Temperate Wetland-Upland Landscapes A. Gallant et al. 10.3390/s18030880
76. Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images Z. Wang et al. 10.3390/rs14143473
77. High-Resolution Flood Monitoring Based on Advanced Statistical Modeling of Sentinel-1 Multi-Temporal Stacks R. Colacicco et al. 10.3390/rs16020294
78. A methodology for mapping annual flood extent using multi-temporal Sentinel-1 imagery T. McCormack et al. 10.1016/j.rse.2022.113273
79. Microwave remote sensing of flood inundation G. Schumann & D. Moller 10.1016/j.pce.2015.05.002
80. Locating flood embankments using SAR time series: A proof of concept M. Wood et al. 10.1016/j.jag.2018.04.003
81. A web application for hydrogeomorphic flood hazard mapping R. Tavares da Costa et al. 10.1016/j.envsoft.2019.04.010
82. Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics R. Tavares da Costa et al. 10.1029/2019WR026453
83. Assessment of spatial–temporal changes in water bodies and its influencing factors using remote sensing and GIS – a model study in the southeast coast of India S. Thirumurthy et al. 10.1007/s10661-022-10228-z
84. Flood mapping under vegetation using single SAR acquisitions S. Grimaldi et al. 10.1016/j.rse.2019.111582
85. 100 Years of Progress in Hydrology C. Peters-Lidard et al. 10.1175/AMSMONOGRAPHS-D-18-0019.1
86. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine B. DeVries et al. 10.1016/j.rse.2020.111664
87. 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
88. Inland Water Body Mapping Using CYGNSS Coherence Detection M. Al-Khaldi et al. 10.1109/TGRS.2020.3047075
89. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data E. Fluet-Chouinard et al. 10.1016/j.rse.2014.10.015
90. Automatic Surface Water Mapping Using Polarimetric SAR Data for Long-Term Change Detection W. Zhang et al. 10.3390/w12030872
91. 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
92. Operational Surface Water Detection and Monitoring Using Radarsat 2 S. Bolanos et al. 10.3390/rs8040285
93. 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
94. Water distribution based on SAR and optical data to improve hazard mapping Y. Han et al. 10.1016/j.envres.2023.116694
95. Flood detection from multi-temporal SAR data using harmonic analysis and change detection S. Schlaffer et al. 10.1016/j.jag.2014.12.001
96. Detection of Inland Open Water Surfaces Using Dual Polarization L-Band Radar for the Soil Moisture Active Passive Mission S. Kim et al. 10.1109/TGRS.2016.2517010
97. Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood S. Sherpa & M. Shirzaei 10.1111/jfr3.12770
98. A High-Resolution Flood Inundation Archive (2016–the Present) from Sentinel-1 SAR Imagery over CONUS Q. Yang et al. 10.1175/BAMS-D-19-0319.1
99. A review and uncertainty analysis of Lake Urmia's hypsometric relationships S. Sima et al. 10.1016/j.jglr.2023.09.010
100. Modelling overbank flood recharge at a continental scale R. Doble et al. 10.5194/hess-18-1273-2014
101. A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains S. Martinis et al. 10.3390/rs5115598
102. Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping S. Martinis et al. 10.1016/j.rse.2022.113077
103. From Global to Local: Providing Actionable Flood Forecast Information in a Cloud‐Based Computing Environment J. Perez et al. 10.1111/1752-1688.12392
104. On the merging of optical and SAR satellite imagery for surface water mapping applications K. Markert et al. 10.1016/j.rinp.2018.02.054
105. Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand R. Westerhoff et al. 10.3390/rs10010058
106. Sentinel-1-based analysis of the severe flood over Pakistan 2022 F. Roth et al. 10.5194/nhess-23-3305-2023
107. Towards Operational Flood Monitoring in Flanders Using Sentinel-1 L. Landuyt et al. 10.1109/JSTARS.2021.3121992
108. Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches L. Landuyt et al. 10.1109/TGRS.2018.2860054
109. Robust algorithm for detecting floodwater in urban areas using synthetic aperture radar images D. Mason et al. 10.1117/1.JRS.12.045011
110. 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
111. Sentinel-1 based Inland water dynamics Mapping System (SIMS) M. Soman & J. Indu 10.1016/j.envsoft.2022.105305
112. Probabilistic Mapping of August 2018 Flood of Kerala, India, Using Space-Borne Synthetic Aperture Radar S. Sherpa et al. 10.1109/JSTARS.2020.2970337
113. Probabilistic mapping of flood-induced backscatter changes in SAR time series S. Schlaffer et al. 10.1016/j.jag.2016.12.003