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Article

Automated Extraction of Surface Water Extent from Sentinel-1 Data

1
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
2
U.S. Fish and Wildlife Service, National Wetlands Inventory, Falls Church, VA 22041, USA
3
U.S. Geological Survey, Eastern Geographic Science Center, Reston, VA 20192, USA
4
School of Environment and Sustainability, University of Saskatchewan, 323 Kirk Hall, 117 Science Place, Saskatoon, SK S7N 5C8, Canada
5
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
6
Science Systems and Applications Inc., Lanham, MD 20706, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 797; https://doi.org/10.3390/rs10050797
Received: 6 March 2018 / Revised: 11 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data. View Full-Text
Keywords: wetlands; surface water extent; inundation; Sentinel-1; synthetic aperture radar (SAR) wetlands; surface water extent; inundation; Sentinel-1; synthetic aperture radar (SAR)
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MDPI and ACS Style

Huang, W.; DeVries, B.; Huang, C.; Lang, M.W.; Jones, J.W.; Creed, I.F.; Carroll, M.L. Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sens. 2018, 10, 797. https://doi.org/10.3390/rs10050797

AMA Style

Huang W, DeVries B, Huang C, Lang MW, Jones JW, Creed IF, Carroll ML. Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sensing. 2018; 10(5):797. https://doi.org/10.3390/rs10050797

Chicago/Turabian Style

Huang, Wenli, Ben DeVries, Chengquan Huang, Megan W. Lang, John W. Jones, Irena F. Creed, and Mark L. Carroll. 2018. "Automated Extraction of Surface Water Extent from Sentinel-1 Data" Remote Sensing 10, no. 5: 797. https://doi.org/10.3390/rs10050797

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