Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery
Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1869 |
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| author | Reda Amer |
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| author_sort | Reda Amer |
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| description | Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R<sup>2</sup> = 0.87, <i>p</i> < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. |
| format | Article |
| id | doaj-art-46bc4c8ec5994cc2a32732fc4e5071ea |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-46bc4c8ec5994cc2a32732fc4e5071ea2025-08-20T02:32:37ZengMDPI AGRemote Sensing2072-42922025-05-011711186910.3390/rs17111869Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR ImageryReda Amer0UMSL Geospatial Collaborative, University of Missouri-St. Louis, St. Louis, MO 63121, USAAccurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R<sup>2</sup> = 0.87, <i>p</i> < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions.https://www.mdpi.com/2072-4292/17/11/1869SAR-based flood mappingSentinel-1 SARmachine learningemergency responserapid inundation detection |
| spellingShingle | Reda Amer Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery Remote Sensing SAR-based flood mapping Sentinel-1 SAR machine learning emergency response rapid inundation detection |
| title | Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery |
| title_full | Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery |
| title_fullStr | Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery |
| title_full_unstemmed | Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery |
| title_short | Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery |
| title_sort | machine learning driven rapid flood mapping for tropical storm imelda using sentinel 1 sar imagery |
| topic | SAR-based flood mapping Sentinel-1 SAR machine learning emergency response rapid inundation detection |
| url | https://www.mdpi.com/2072-4292/17/11/1869 |
| work_keys_str_mv | AT redaamer machinelearningdrivenrapidfloodmappingfortropicalstormimeldausingsentinel1sarimagery |