Paper Info
Title
Automated Extraction of Surface Water Extent from Sentinel-1 Data.
Abstract
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.
Year
DOI
Venue
2018
10.3390/rs10050797
REMOTE SENSING
Keywords
Field
DocType
wetlands,surface water extent,inundation,Sentinel-1,synthetic aperture radar (SAR)
Peninsula,Synthetic aperture radar,Surface water,Remote sensing,Wetland,Shuttle Radar Topography Mission,Geology,Water body,Temporal resolution,Decision tree learning
Journal
Volume
Issue
Citations 
10
5
4
PageRank 
References 
Authors
0.42
15
7
Name
Order
Citations
PageRank
Wenli Huang1305.75
Ben DeVries292.03
Chengquan Huang322636.44
Megan Lang4141.88
John W. Jones5236.20
Irena F. Creed6182.56
mark carroll7143.75