The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas
Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/10/4/583 |
| _version_ | 1818321871383298048 |
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| author | Sandro Martinis Simon Plank Kamila Ćwik |
| author_facet | Sandro Martinis Simon Plank Kamila Ćwik |
| author_sort | Sandro Martinis |
| collection | DOAJ |
| description | Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood processing chain by removing overestimations of the water extent related to low-backscattering sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series statistics of Sentinel-1 data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia and Ethiopia, which has been covered by a time-series of 202 Sentinel-1 scenes within the period June 2014 to May 2017. The approach proved capable of significantly improving the classification accuracy of the Sentinel-1 flood service within this study site. The Overall Accuracy increased by ~5% to a value of 98.5% and the User’s Accuracy increased by 25.2% to a value of 96.0%. Experimental results have shown that the classification accuracy is influenced by several parameters such as the lengths of the time-series used for generating the SEL. |
| first_indexed | 2024-12-13T10:47:47Z |
| format | Article |
| id | doaj.art-1cf38cfb174b4777b0f2d400e65269c9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:47:47Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1cf38cfb174b4777b0f2d400e65269c92022-12-21T23:50:03ZengMDPI AGRemote Sensing2072-42922018-04-0110458310.3390/rs10040583rs10040583The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid AreasSandro Martinis0Simon Plank1Kamila Ćwik2German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, GermanyDue to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood processing chain by removing overestimations of the water extent related to low-backscattering sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series statistics of Sentinel-1 data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia and Ethiopia, which has been covered by a time-series of 202 Sentinel-1 scenes within the period June 2014 to May 2017. The approach proved capable of significantly improving the classification accuracy of the Sentinel-1 flood service within this study site. The Overall Accuracy increased by ~5% to a value of 98.5% and the User’s Accuracy increased by 25.2% to a value of 96.0%. Experimental results have shown that the classification accuracy is influenced by several parameters such as the lengths of the time-series used for generating the SEL.http://www.mdpi.com/2072-4292/10/4/583SAR (Synthetic Aperture Radar)water bodiesinundationflood detectionSentinel-1time-seriessand surfacesarid areas |
| spellingShingle | Sandro Martinis Simon Plank Kamila Ćwik The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas Remote Sensing SAR (Synthetic Aperture Radar) water bodies inundation flood detection Sentinel-1 time-series sand surfaces arid areas |
| title | The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas |
| title_full | The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas |
| title_fullStr | The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas |
| title_full_unstemmed | The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas |
| title_short | The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas |
| title_sort | use of sentinel 1 time series data to improve flood monitoring in arid areas |
| topic | SAR (Synthetic Aperture Radar) water bodies inundation flood detection Sentinel-1 time-series sand surfaces arid areas |
| url | http://www.mdpi.com/2072-4292/10/4/583 |
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