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Remote Sens. 2019, 11(3), 327; https://doi.org/10.3390/rs11030327

Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

1
Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China
2
Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
3
Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
4
Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo Urban Environment Observation and Research Station, Chinese Academy of Sciences, Ningbo 315830, China
5
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
6
Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China
7
School of Urban & Rural Planning and Landscape Architecture, Xuchang University, Xuchang 461000, China
*
Author to whom correspondence should be addressed.
Received: 5 January 2019 / Revised: 30 January 2019 / Accepted: 2 February 2019 / Published: 7 February 2019
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Abstract

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image. View Full-Text
Keywords: Sentinel-3; water body mapping; Normalized Difference Water Index (NDWI); sub-pixel mapping; Fuzzy C-means clustering (FCM); Unsupervised Sentinel-3; water body mapping; Normalized Difference Water Index (NDWI); sub-pixel mapping; Fuzzy C-means clustering (FCM); Unsupervised
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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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Wang, X.; Ling, F.; Yao, H.; Liu, Y.; Xu, S. Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image. Remote Sens. 2019, 11, 327.

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