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Remote Sens. 2017, 9(6), 596;

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

ICube Laboratory, University of Strasbourg, 67081 Strasbourg, France
School of Earth & Space Sciences, Peking University, Beijing 100080, China
Depart of Computing Science, University of Alberta, Edmonton, T6G 2R3, Canada
School of Software, Yunnan University, Kunming 650091, China
School of Software & Microelectronucs, Peking University, Beijing 100080, China
Author to whom correspondence should be addressed.
Academic Editors: Claudia Kuenzer, Deepak R. Mishra, Weimin Huang and Prasad S. Thenkabail
Received: 22 March 2017 / Revised: 14 May 2017 / Accepted: 10 June 2017 / Published: 12 June 2017
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This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands in 10 m and Short-Wavelength InfraRed (SWIR) bands in 20 m, which are closely related to surface water information. It is necessary to define a pan-like band for the Sentinel-2 image sharpening process because of the replacement of the panchromatic band by four high-resolution multi-spectral bands (10 m). This study, which aimed at urban surface water extraction, utilised the Normalised Difference Water Index (NDWI) at 10 m resolution as a high-resolution image to sharpen the 20 m SWIR bands. Then, object-level Modified NDWI (MNDWI) mapping and minimum valley bottom adjustment threshold were applied to extract water maps. The proposed method was compared with the conventional most related band- (between the visible spectrum/NIR and SWIR bands) based and principal component analysis first component-based sharpening. Results show that the proposed NDWI-based MNDWI image exhibits higher separability and is more effective for both classification-level and boundary-level final water maps than traditional approaches. View Full-Text
Keywords: water extraction; water indices; Sentinel-2; multi-spectral remote sensing mapping water extraction; water indices; Sentinel-2; multi-spectral remote sensing mapping

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, X.; Zhao, S.; Qin, X.; Zhao, N.; Liang, L. Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens. 2017, 9, 596.

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