Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due...
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/18/3026 |
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| author | S L Kesav Unnithan Basudev Biswal Christoph Rüdiger |
| author_facet | S L Kesav Unnithan Basudev Biswal Christoph Rüdiger |
| author_sort | S L Kesav Unnithan |
| collection | DOAJ |
| description | The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19. |
| first_indexed | 2024-03-10T16:16:59Z |
| format | Article |
| id | doaj.art-8d6b690765844d038fc57cab11c2ad3f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:16:59Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8d6b690765844d038fc57cab11c2ad3f2023-11-20T14:00:14ZengMDPI AGRemote Sensing2072-42922020-09-011218302610.3390/rs12183026Flood Inundation Mapping by Combining GNSS-R Signals with Topographical InformationS L Kesav Unnithan0Basudev Biswal1Christoph Rüdiger2IITB-Monash Research Academy, Mumbai 400076, IndiaDepartment of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, IndiaDepartment of Civil Engineering, Monash University, Clayton, VIC 3168, AustraliaThe Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19.https://www.mdpi.com/2072-4292/12/18/3026CYGNSSHANDflood inundation mappingSentinel-1A SAR |
| spellingShingle | S L Kesav Unnithan Basudev Biswal Christoph Rüdiger Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information Remote Sensing CYGNSS HAND flood inundation mapping Sentinel-1A SAR |
| title | Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information |
| title_full | Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information |
| title_fullStr | Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information |
| title_full_unstemmed | Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information |
| title_short | Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information |
| title_sort | flood inundation mapping by combining gnss r signals with topographical information |
| topic | CYGNSS HAND flood inundation mapping Sentinel-1A SAR |
| url | https://www.mdpi.com/2072-4292/12/18/3026 |
| work_keys_str_mv | AT slkesavunnithan floodinundationmappingbycombininggnssrsignalswithtopographicalinformation AT basudevbiswal floodinundationmappingbycombininggnssrsignalswithtopographicalinformation AT christophrudiger floodinundationmappingbycombininggnssrsignalswithtopographicalinformation |