55 citations as recorded by crossref.

1. Hydrologic balance and inundation dynamics of Southeast Asia's largest inland lake altered by hydropower dams in the Mekong River basin H. Dang et al. https://doi.org/10.1016/j.scitotenv.2022.154833
2. 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
3. Priority Analysis of Remote Sensing and Geospatial Information Techniques to Water-Related Disaster Damage Reduction for Inter-Korean Cooperation S. Lee et al. https://doi.org/10.1155/2020/8878888
4. Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas S. Hu et al. https://doi.org/10.3390/rs12020243
5. 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
6. 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
7. High Resolution 3D Mapping of Hurricane Flooding from Moderate-Resolution Operational Satellites S. Li et al. https://doi.org/10.3390/rs14215445
8. Gridded flood depth estimates from satellite-derived inundations S. Bryant et al. https://doi.org/10.5194/nhess-22-1437-2022
9. Enhanced flood mapping using synthetic aperture radar (SAR) images, hydraulic modelling, and social media: A case study of Hurricane Harvey (Houston, TX) V. Scotti et al. https://doi.org/10.1111/jfr3.12647
10. Basin-wide flood depth and exposure mapping from SAR images and machine learning models C. Hao et al. https://doi.org/10.1016/j.jenvman.2021.113367
11. 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
12. 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
13. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston F. Brill et al. https://doi.org/10.3390/rs13112042
14. Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data P. van Rutten et al. https://doi.org/10.3390/rs17132171
15. Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. https://doi.org/10.1109/TGRS.2022.3147429
16. Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi L. Wouters et al. https://doi.org/10.5194/nhess-21-3199-2021
17. Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques A. Khoshkonesh et al. https://doi.org/10.1016/j.scitotenv.2024.175859
18. Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo https://doi.org/10.3390/s21165614
19. A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods M. Lahsaini et al. https://doi.org/10.3390/rs16122193
20. Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy G. Petropoulos et al. https://doi.org/10.3390/geohazards5020025
21. DEM Generation Incorporating River Channels in Data-Scarce Contexts: The “Fluvial Domain Method” J. Villanueva et al. https://doi.org/10.3390/hydrology12020033
22. Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms I. Elkhrachy https://doi.org/10.3390/rs14030440
23. BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation B. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.132139
24. Combining SAR and Optical Earth Observation with Hydraulic Simulation for Flood Mapping and Impact Assessment E. Psomiadis et al. https://doi.org/10.3390/rs12233980
25. Prolonged coastal inundation detected with synthetic aperture radar significantly retarded functional recovery of mangroves after major hurricanes M. Yu & Q. Gao https://doi.org/10.1007/s10980-022-01540-7
26. 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
27. Interannual comparison of historical floods through flood detection using multi-temporal Sentinel-1 SAR images, Awash River Basin, Ethiopia A. Tamiru Haile et al. https://doi.org/10.1016/j.jag.2023.103505
28. Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review P. Tavares et al. https://doi.org/10.3390/environments6050051
29. Flood susceptibility mapping in El Niño Phenomenom integrating multitemporal radar analysis, GIS and machine learning techniques, Piura river basin, Peru E. Badillo-Rivera et al. https://doi.org/10.3389/fenvs.2025.1672107
30. FlDepth: A New Method for Estimating Fluvial and Pluvial Flood Depths from Near Real-Time Satellite-Derived Inundation Map and Topography A. Akkimi et al. https://doi.org/10.1007/s11269-025-04405-1
31. INterpolated FLOod Surface (INFLOS), a Rapid and Operational Tool to Estimate Flood Depths from Earth Observation Data for Emergency Management Q. Poterek et al. https://doi.org/10.3390/rs17020329
32. Hydraulic Model Calibration Using Water Levels Derived from Time Series High-Resolution SAR Images N. Desrochers et al. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001687
33. Automated floodwater depth estimation using large multimodal model for rapid flood mapping T. Akinboyewa et al. https://doi.org/10.1007/s43762-024-00123-3
34. A Comprehensive Review of Machine Learning Approaches for Flood Depth Estimation B. Liu et al. https://doi.org/10.1007/s13753-025-00639-0
35. Flood Depth Estimation in Agricultural Lands From L and C-Band Synthetic Aperture Radar Images and Digital Elevation Model S. Surampudi & V. Kumar https://doi.org/10.1109/ACCESS.2023.3234742
36. Performance Evaluation of 3-D Convolutional Neural Network for Multitemporal Flood Classification Framework With Synthetic Aperture Radar Image Data D. Sudiana et al. https://doi.org/10.1109/JSTARS.2024.3519523
37. Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos B. Alizadeh et al. https://doi.org/10.1016/j.aei.2022.101730
38. 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
39. Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al. https://doi.org/10.1029/2022WR033155
40. Flood hazards and socioeconomic vulnerability across Arkansas: A geospatial cyberinfrastructure B. Peter et al. https://doi.org/10.1016/j.pdisas.2026.100591
41. The use of remote sensing indices to understand flood‐recharged soil moisture impacts on trees in semi‐arid floodplains: A review E. Pedzisai et al. https://doi.org/10.1002/eco.2460
42. Space-Based Detection of Significant Water-Depth Increase Induced by Hurricane Irma in the Everglades Wetlands Using Sentinel-1 SAR Backscatter Observations B. Zhang et al. https://doi.org/10.3390/rs14061415
43. 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
44. Vulnerability and risk assessment mapping of Bhitarkanika national park, Odisha, India using machine-based embedded decision support system S. Mohanty et al. https://doi.org/10.3389/fenvs.2023.1176547
45. 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
46. Inundation Extent Mapping by Synthetic Aperture Radar: A Review X. Shen et al. https://doi.org/10.3390/rs11070879
47. Integration of earth observation and census data for mapping a multi-temporal flood vulnerability index: a case study on Northeast Italy F. Cian et al. https://doi.org/10.1007/s11069-021-04535-w
48. Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model A. Gebrehiwot & L. Hashemi-Beni https://doi.org/10.3390/ijgi10030144
49. Runoff Estimation in Ungauged Watershed and Sensitivity Analysis According to the Soil Characteristics: Case Study of the Saint Blaise Vallon in France G. Park et al. https://doi.org/10.3390/su14169848
50. A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling W. Tan et al. https://doi.org/10.1016/j.rse.2023.113956
51. Flood Inundation Mapping with Supervised Classifiers: 2021 Gediz Plain Flood E. ARSLAN & S. KARTAL https://doi.org/10.48123/rsgis.1220879
52. Detection of Flood Extent Using Sentinel-1A/B Synthetic Aperture Radar: An Application for Hurricane Harvey, Houston, TX K. Tiampo et al. https://doi.org/10.3390/rs14092261
53. Urban Road Anomaly Monitoring Using Vision–Language Models for Enhanced Safety Management H. Ding et al. https://doi.org/10.3390/app15052517
54. Reconstructing flood level timeseries at seasonal wetlands in Ireland using Sentinel-1 T. McCormack et al. https://doi.org/10.1016/j.rse.2023.113839
55. 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