Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-2473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Aug 2022
Research article |
| 02 Aug 2022
| 02 Aug 2022
Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe
Angelica Tarpanelli
CORRESPONDING AUTHOR
Research Institute for Geo-Hydrological Protection, National Research
Council, Via Madonna Alta 126, 06128 Perugia, Italy
Alessandro C. Mondini
Research Institute for Geo-Hydrological Protection, National Research
Council, Via Madonna Alta 126, 06128 Perugia, Italy
Stefania Camici
Research Institute for Geo-Hydrological Protection, National Research
Council, Via Madonna Alta 126, 06128 Perugia, Italy
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Cited
22 citations as recorded by crossref.
- 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
- A new European coastal flood database for low–medium intensity events M. Le Gal et al. 10.5194/nhess-23-3585-2023
- 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
- Extreme Coastal Flood Inundation Mapping Based on Sentinel 1 Using Google Earth Engine E. Wijayanti et al. 10.1051/e3sconf/202346804002
- Cross-modal distillation for flood extent mapping S. Garg et al. 10.1017/eds.2023.34
- Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
- Open-access remote sensing data for cooperation in transboundary water management S. Yalew et al. 10.1080/02508060.2023.2263226
- Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms J. Soria-Ruiz et al. 10.3390/atmos13111852
- Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia A. Rimba et al. 10.3390/w15213783
- Flood Image Classification using Convolutional Neural Networks O. Adetunji et al. 10.53982/ajerd.2023.0602.11-j
- 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
- 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
- Flood Mapping and Damage Assessment using Ensemble Model Approach V. Patil et al. 10.1007/s11220-024-00464-7
- Characteristics, drivers, and predictability of flood events in the Tana River Basin, Kenya A. Kiptum et al. 10.1016/j.ejrh.2024.101748
- 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
- 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
- Limitations in the use of Sentinel-1 data for morphological change detection in rivers G. Marchetti et al. 10.1080/01431161.2023.2274317
- 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
- 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
- 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
- Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe A. Tarpanelli et al. 10.5194/nhess-22-2473-2022
- 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
20 citations as recorded by crossref.
- 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
- A new European coastal flood database for low–medium intensity events M. Le Gal et al. 10.5194/nhess-23-3585-2023
- 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
- Extreme Coastal Flood Inundation Mapping Based on Sentinel 1 Using Google Earth Engine E. Wijayanti et al. 10.1051/e3sconf/202346804002
- Cross-modal distillation for flood extent mapping S. Garg et al. 10.1017/eds.2023.34
- Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
- Open-access remote sensing data for cooperation in transboundary water management S. Yalew et al. 10.1080/02508060.2023.2263226
- Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms J. Soria-Ruiz et al. 10.3390/atmos13111852
- Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia A. Rimba et al. 10.3390/w15213783
- Flood Image Classification using Convolutional Neural Networks O. Adetunji et al. 10.53982/ajerd.2023.0602.11-j
- 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
- 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
- Flood Mapping and Damage Assessment using Ensemble Model Approach V. Patil et al. 10.1007/s11220-024-00464-7
- Characteristics, drivers, and predictability of flood events in the Tana River Basin, Kenya A. Kiptum et al. 10.1016/j.ejrh.2024.101748
- 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
- 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
- Limitations in the use of Sentinel-1 data for morphological change detection in rivers G. Marchetti et al. 10.1080/01431161.2023.2274317
- 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
- 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
- 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
2 citations as recorded by crossref.
- Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe A. Tarpanelli et al. 10.5194/nhess-22-2473-2022
- 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
Latest update: 26 Apr 2024
Short summary
We analysed 10 years of river discharge data from almost 2000 sites in Europe, and we extracted flood events, as proxies of flood inundations, based on the overpasses of Sentinel-1 and Sentinel-2 satellites to derive the percentage of potential inundation events that they were able to observe. Results show that on average 58 % of flood events are potentially observable by Sentinel-1 and only 28 % by Sentinel-2 due to the obstacle of cloud coverage.
We analysed 10 years of river discharge data from almost 2000 sites in Europe, and we extracted...
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