36 citations as recorded by crossref.

1. Comparison of Ratioing and RCNA Methods in the Detection of Flooded Areas Using Sentinel 2 Imagery (Case Study: Tulun, Russia) H. Fernandez et al. 10.3390/su151310233
2. Digital mapping of dates of transplanting and accumulated thermal requirement of rice ( Oryza sativa L.) in the subtropics of North Eastern Hill Region, India S. Panda et al. 10.1080/22797254.2024.2406796
3. Optimum flood inundation mapping in mountainous regions using Sentinel-1 data and a GIS-based multi-criteria approach: a case study of Tlawng river basin, Mizoram, India S. Debbarma et al. 10.1007/s10661-024-13437-w
4. Cross-modal distillation for flood extent mapping S. Garg et al. 10.1017/eds.2023.34
5. One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam P. Hoa et al. 10.1007/s12145-024-01285-8
6. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
7. Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia A. Rimba et al. 10.3390/w15213783
8. Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy G. Petropoulos et al. 10.3390/geohazards5020025
9. A framework and pilot study for assessing usability of flood data portals for interdisciplinary research B. Langlois et al. 10.1371/journal.pclm.0000511
10. Modeling surge dynamics improves coastal flood estimates in a global set of tropical cyclones T. Vogt et al. 10.1038/s43247-024-01707-x
11. Hydrometeorological Extreme Events in Africa: The Role of Satellite Observations for Monitoring Pluvial and Fluvial Flood Risk M. Gosset et al. 10.1007/s10712-022-09749-6
12. Characteristics, drivers, and predictability of flood events in the Tana River Basin, Kenya A. Kiptum et al. 10.1016/j.ejrh.2024.101748
13. Flooding in the Digital Twin Earth: The Case Study of the Enza River Levee Breach in December 2017 A. Tarpanelli et al. 10.3390/w15091644
14. Limitations in the use of Sentinel-1 data for morphological change detection in rivers G. Marchetti et al. 10.1080/01431161.2023.2274317
15. Reconstructing Snow-Free Sentinel-2 Satellite Imagery: A Generative Adversarial Network (GAN) Approach T. Oluwadare et al. 10.3390/rs16132352
16. A multi-sensor approach for increased measurements of floods and their societal impacts from space D. Munasinghe et al. 10.1038/s43247-023-01129-1
17. Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India S. Koley & S. Kumar 10.1007/s10661-024-12667-2
18. Better localized predictions with Out-of-Scope information and Explainable AI: One-Shot SAR backscatter nowcast framework with data from neighboring region Z. Li & I. Demir 10.1016/j.isprsjprs.2023.11.021
19. Combining multisensor images and social network data to assess the area flooded by a hurricane event R. Hernández-Guzmán & A. Ruiz-Luna 10.7717/peerj.17319
20. A new European coastal flood database for low–medium intensity events M. Le Gal et al. 10.5194/nhess-23-3585-2023
21. A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios K. Yao et al. 10.3390/rs16081413
22. Extreme Coastal Flood Inundation Mapping Based on Sentinel 1 Using Google Earth Engine E. Wijayanti et al. 10.1051/e3sconf/202346804002
23. Open-access remote sensing data for cooperation in transboundary water management S. Yalew et al. 10.1080/02508060.2023.2263226
24. Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms J. Soria-Ruiz et al. 10.3390/atmos13111852
25. 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
26. Unsupervised Color-Based Flood Segmentation in UAV Imagery G. Simantiris & C. Panagiotakis 10.3390/rs16122126
27. Flood Image Classification using Convolutional Neural Networks O. Adetunji et al. 10.53982/ajerd.2023.0602.11-j
28. The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya F. Mitheu et al. 10.1111/jfr3.12911
29. Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels M. Li et al. 10.1016/j.jhydrol.2024.131913
30. Flood Mapping and Damage Assessment using Ensemble Model Approach V. Patil et al. 10.1007/s11220-024-00464-7
31. Flood monitoring in an Giang Province, Vietnam using global flood mapper and Sentinel-1 SAR A. Afifi & A. Magdy 10.1080/2150704X.2024.2388846
32. MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces Z. Li et al. 10.1016/j.isprsjprs.2023.10.002
33. Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India S. Saravanan et al. 10.1016/j.uclim.2023.101503
34. Rapid Extreme Tropical Precipitation and Flood Inundation Mapping Framework (RETRACE): Initial Testing for the 2021–2022 Malaysia Flood Y. Tew et al. 10.3390/ijgi11070378
35. Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe A. Tarpanelli et al. 10.5194/nhess-22-2473-2022
36. Flood impact assessment on agricultural and municipal areas using Sentinel-1 and 2 satellite images (case study: Kermanshah province) S. Gord et al. 10.1007/s11069-024-06514-3