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Article

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

1
ICube Laboratory, University of Strasbourg, 67081 Strasbourg, France
2
School of Earth & Space Sciences, Peking University, Beijing 100080, China
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Depart of Computing Science, University of Alberta, Edmonton, T6G 2R3, Canada
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School of Software, Yunnan University, Kunming 650091, China
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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
Remote Sens. 2017, 9(6), 596; https://doi.org/10.3390/rs9060596
Received: 22 March 2017 / Revised: 14 May 2017 / Accepted: 10 June 2017 / Published: 12 June 2017
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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MDPI and ACS Style

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. https://doi.org/10.3390/rs9060596

AMA Style

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 Sensing. 2017; 9(6):596. https://doi.org/10.3390/rs9060596

Chicago/Turabian Style

Yang, Xiucheng, Shanshan Zhao, Xuebin Qin, Na Zhao, and Ligang Liang. 2017. "Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening" Remote Sensing 9, no. 6: 596. https://doi.org/10.3390/rs9060596

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