{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T23:37:03Z","timestamp":1773013023180,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,7]],"date-time":"2019-02-07T00:00:00Z","timestamp":1549497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDA2003030201"],"award-info":[{"award-number":["XDA2003030201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs11030327","type":"journal-article","created":{"date-parts":[[2019,2,7]],"date-time":"2019-02-07T11:50:33Z","timestamp":1549540233000},"page":"327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9258-5477","authenticated-orcid":false,"given":"Xia","family":"Wang","sequence":"first","affiliation":[{"name":"Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"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"}]},{"given":"Huaiying","family":"Yao","sequence":"additional","affiliation":[{"name":"Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"},{"name":"Key Laboratory of Urban Environmental Processes and Pollution Control, Ningbo Urban Environment Observation and Research Station, Chinese Academy of Sciences, Ningbo 315830, China"}]},{"given":"Yaolin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China"}]},{"given":"Shuna","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Urban &amp; Rural Planning and Landscape Architecture, Xuchang University, Xuchang 461000, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1109\/TGRS.2009.2019126","article-title":"Quantification of the effects of Land-Cover-Class spectral separability on the accuracy of markov-Random-Field-Based superresolution mapping","volume":"47","author":"Tolpekin","year":"2009","journal-title":"IEEE Trans. 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