Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data
In the Asia–Pacific, where rice is an essential crop for food security and economic activity, tropical cyclones and consecutive floods can cause substantial damage to rice fields. Humanitarian organizations have developed impact-based forecasting models to be able to trigger early actions before flo...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2171 |
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| author | Pepijn van Rutten Irene Benito Lazaro Sanne Muis Aklilu Teklesadik Marc van den Homberg |
| author_facet | Pepijn van Rutten Irene Benito Lazaro Sanne Muis Aklilu Teklesadik Marc van den Homberg |
| author_sort | Pepijn van Rutten |
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| description | In the Asia–Pacific, where rice is an essential crop for food security and economic activity, tropical cyclones and consecutive floods can cause substantial damage to rice fields. Humanitarian organizations have developed impact-based forecasting models to be able to trigger early actions before floods arrive. In this study we show how Sentinel-1 SAR data and Otsu thresholding can be used to estimate flooding and damage caused to rice fields, using the case study of tropical storm Talas (2017). The current most accurate global Digital Elevation Model FABDEM was used to derive flood depths. Subsequently, rice yield loss curves and rice field maps were used to estimate economic damage. Our analysis results in a total of 475 km<sup>2</sup> of inundated rice fields in seven Northern Vietnam provinces. Flood depths were mostly shallow, with 2 km<sup>2</sup> having a flood depth of more than 0.5 m. Using these flood extent and depth values with rice damage curves results in lower damage values than the ones based on ground reporting, indicating a likely underestimation of flood depth. However, this study demonstrates that Sentinel-1-derived flood maps with the high-resolution DEM can deliver rapid damage estimates, also for those areas where there is no ground-based reporting of rice damage, showing its potential to be used in impact-based forecasting model training. |
| format | Article |
| id | doaj-art-e0bc21e7dbaf488dbe435b7f49f1f1a9 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e0bc21e7dbaf488dbe435b7f49f1f1a92025-08-20T02:36:30ZengMDPI AGRemote Sensing2072-42922025-06-011713217110.3390/rs17132171Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR DataPepijn van Rutten0Irene Benito Lazaro1Sanne Muis2Aklilu Teklesadik3Marc van den Homberg4Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The NetherlandsInstitute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The NetherlandsInstitute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands510, An Initiative of the Netherlands Red Cross, 2593 HT The Hague, The Netherlands510, An Initiative of the Netherlands Red Cross, 2593 HT The Hague, The NetherlandsIn the Asia–Pacific, where rice is an essential crop for food security and economic activity, tropical cyclones and consecutive floods can cause substantial damage to rice fields. Humanitarian organizations have developed impact-based forecasting models to be able to trigger early actions before floods arrive. In this study we show how Sentinel-1 SAR data and Otsu thresholding can be used to estimate flooding and damage caused to rice fields, using the case study of tropical storm Talas (2017). The current most accurate global Digital Elevation Model FABDEM was used to derive flood depths. Subsequently, rice yield loss curves and rice field maps were used to estimate economic damage. Our analysis results in a total of 475 km<sup>2</sup> of inundated rice fields in seven Northern Vietnam provinces. Flood depths were mostly shallow, with 2 km<sup>2</sup> having a flood depth of more than 0.5 m. Using these flood extent and depth values with rice damage curves results in lower damage values than the ones based on ground reporting, indicating a likely underestimation of flood depth. However, this study demonstrates that Sentinel-1-derived flood maps with the high-resolution DEM can deliver rapid damage estimates, also for those areas where there is no ground-based reporting of rice damage, showing its potential to be used in impact-based forecasting model training.https://www.mdpi.com/2072-4292/17/13/2171impact-based forecastingsentinel-1SARfloodingriceVietnam |
| spellingShingle | Pepijn van Rutten Irene Benito Lazaro Sanne Muis Aklilu Teklesadik Marc van den Homberg Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data Remote Sensing impact-based forecasting sentinel-1 SAR flooding rice Vietnam |
| title | Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data |
| title_full | Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data |
| title_fullStr | Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data |
| title_full_unstemmed | Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data |
| title_short | Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data |
| title_sort | flood and rice damage mapping for tropical storm talas in vietnam using sentinel 1 sar data |
| topic | impact-based forecasting sentinel-1 SAR flooding rice Vietnam |
| url | https://www.mdpi.com/2072-4292/17/13/2171 |
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