Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/rs10040583 | REMOTE SENSING |
Keywords | Field | DocType |
SAR (Synthetic Aperture Radar),water bodies,inundation,flood detection,Sentinel-1,time-series,sand surfaces,arid areas | Radar,Time series,Data set,Satellite,Radar backscatter,Arid,Remote sensing,Geology,Flood myth,Open water | Journal |
Volume | Issue | ISSN |
10 | 4 | 2072-4292 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandro Martinis | 1 | 86 | 12.98 |
| Simon Plank | 2 | 11 | 3.16 |
| Kamila Cwik | 3 | 2 | 0.38 |