{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:59:34Z","timestamp":1768679974499,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,12]],"date-time":"2016-07-12T00:00:00Z","timestamp":1468281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013105","name":"Shanghai Rising-Star Program","doi-asserted-by":"publisher","award":["15QA1403700"],"award-info":[{"award-number":["15QA1403700"]}],"id":[{"id":"10.13039\/501100013105","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41201426, 41325005, 41571407 and 41611130113"],"award-info":[{"award-number":["41201426, 41325005, 41571407 and 41611130113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2012CB957701"],"award-info":[{"award-number":["2012CB957701"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities:"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; however, when applied to urban areas, this spatially- explicit approach is a challenging task due to the fact that the water bodies are often of a small size and spectral confusion is common between water and the complex features in the urban environment. Water indexes are the most common method of water extraction at the pixel level. More recently, spectral mixture analysis (SMA) has been widely employed in analyzing the urban environment at the subpixel level. The objective of this study is to develop an automatic subpixel water mapping method (ASWM) which can achieve a high accuracy in urban areas. Specifically, we first apply a water index for the automatic extraction of mixed land-water pixels, and the pure water pixels that are generated in this process are exported as the final result. Secondly, the SMA technique is applied to the mixed land-water pixels for water abundance estimation. As for obtaining the most representative endmembers, we propose an adaptive iterative endmember selection method based on the spatial similarity of adjacent ground surfaces. One classical water index method (the modified normalized difference water index (MNDWI)), a pixel-level target detection method (constrained energy minimization (CEM)), and two widely used SMA methods (fully constrained least squares (FCLS) and multiple endmember spectral mixture analysis (MESMA)) were chosen for the water mapping comparison in the experiments. The results indicate that the proposed ASWM was able to detect water pixels more efficiency than other unsupervised water extraction methods, and the water fractions estimated by the proposed ASWM method correspond closely to the reference fractions with the slopes of 0.97, 1.02, 1.04, and 0.98 and the R-squared values of 0.9454, 0.9486, 0.9665, and 0.9607 in regression analysis corresponding to different test regions. In the quantitative accuracy assessment, the ASWM method shows the best performance in water mapping with the mean kappa coefficient of 0.862, mean producer\u2019s accuracy of 82.8%, and mean user\u2019s accuracy of 91.8% for test regions.<\/jats:p>","DOI":"10.3390\/rs8070584","type":"journal-article","created":{"date-parts":[[2016,7,12]],"date-time":"2016-07-12T10:01:21Z","timestamp":1468317681000},"page":"584","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery"],"prefix":"10.3390","volume":"8","author":[{"given":"Huan","family":"Xie","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4907-162X","authenticated-orcid":false,"given":"Xin","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Xiong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Haiyan","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Xiaohua","family":"Tong","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,12]]},"reference":[{"key":"ref_1","first-page":"536","article-title":"Rapid response flood detection using the MSG geostationary satellite","volume":"13","author":"Proud","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.1016\/j.rse.2011.06.013","article-title":"MODIS observations of the bottom topography and its inter-annual variability of Poyang Lake","volume":"115","author":"Feng","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1029\/2012GL051276","article-title":"Changes in land surface water dynamics since the 1990s and relation to population pressure","volume":"39","author":"Prigent","year":"2012","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.rse.2013.01.009","article-title":"Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western\u2013northwestern Black Sea coastal zone","volume":"132","author":"Niculescu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1080\/01431160902994440","article-title":"Quantitative monitoring of inland water using remote sensing: An example of upper reaches of Huangpu River, China","volume":"31","author":"Tong","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","first-page":"691","article-title":"Coastline change assessment at the Aegean Sea Coasts in Turkey using multitemporal Landsat imagery","volume":"23","author":"Ekercin","year":"2009","journal-title":"J. Coast. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2012.02.024","article-title":"Automatic extraction of shorelines from Landsat TM and ETM multi-temporal images with subpixel precision","volume":"123","author":"Ruiz","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1080\/0143116042000298324","article-title":"Mapping and visualizing the Great Salt Lake landscape dynamics using multi-temporal satellite images, 1972\u20131996","volume":"26","author":"Hung","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2013.01.010","article-title":"Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011","volume":"79","author":"Tulbure","year":"2013","journal-title":"ISPRS J. Photogramm."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/S0034-4257(02)00053-6","article-title":"Investigation of flood inundation on playas within the Zone of Chotts, using a time-series of AVHRR","volume":"82","author":"Bryant","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s11269-005-3281-5","article-title":"Delineation of flood-prone areas using remote sensing technique","volume":"19","author":"Jain","year":"2005","journal-title":"Water Resour. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of normalized difference water index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"5230","article-title":"New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images","volume":"8","author":"Xie","year":"2014","journal-title":"J. Appl. Remote Sen."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2015.10.005","article-title":"Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach","volume":"171","author":"Yang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","first-page":"616","article-title":"Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data","volume":"13","author":"Ghrefat","year":"2011","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.3390\/rs70201640","article-title":"An Improved Unmixing-Based Fusion Method: Potential application to remote monitoring of inland waters","volume":"7","author":"Guo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10236-010-0373-4","article-title":"Spectra of a shallow sea-unmixing for class identification and monitoring of coastal waters","volume":"61","author":"Hommersom","year":"2011","journal-title":"Ocean. Dyn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/TGRS.2014.2346535","article-title":"Fast subpixel mapping algorithms for subpixel resolution change detection","volume":"53","author":"Wang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1109\/JSTARS.2014.2355832","article-title":"Land cover change detection at subpixel resolution with a Hopfield neural network","volume":"8","author":"Wang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.034","article-title":"Incorporating spatial information in spectral unmixing: A review","volume":"149","author":"Shi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1109\/TGRS.2011.2162098","article-title":"A hybrid automatic endmember extraction algorithm based on a local window","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in Spectral Mixture Analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1109\/TGRS.2012.2219058","article-title":"Piecewise convex multiple-model endmember detection and spectral unmixing","volume":"51","author":"Zare","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/JSTARS.2011.2125778","article-title":"Deriving water fraction and flood maps from MODIS images using a decision tree approach","volume":"4","author":"Sun","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","first-page":"109","article-title":"Locally adaptive unmixing method for lake-water area extraction based on MODIS 250 m bands","volume":"33","author":"Ma","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"277","DOI":"10.2747\/1548-1603.42.4.277","article-title":"Remote detection of Prairie Pothole Ponds in the Devils Lake Basin, North Dakota","volume":"42","author":"Sethre","year":"2005","journal-title":"GISci. Remote Sens."},{"key":"ref_29","unstructured":"Applied Analysis Inc. (1997). Subpixel Classifier for IMAGINE: User\u2019s Guide, Applied Analysis Inc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.14358\/PERS.75.11.1307","article-title":"Analysis of dynamic thresholds for the normalized difference water index","volume":"75","author":"Ji","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1080\/01431161.2016.1168948","article-title":"Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction","volume":"37","author":"Xie","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","article-title":"Robust locally weighted regression and smoothing scatterplots","volume":"74","author":"Cleveland","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2013.02.005","article-title":"A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution","volume":"133","author":"Deng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2006.09.005","article-title":"Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil","volume":"106","author":"Powell","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.07.021","article-title":"Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission","volume":"117","author":"Roberts","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.08.003","article-title":"Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery","volume":"93","author":"Wu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1016\/j.rse.2009.05.014","article-title":"Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating impervious surface distribution by spectral mixture analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping Chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1016\/j.rse.2009.03.018","article-title":"Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments","volume":"113","author":"Franke","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0034-4257(03)00135-4","article-title":"Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE","volume":"87","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"024023","DOI":"10.1088\/1748-9326\/6\/2\/024023","article-title":"Estimation of chlorophyll-a concentration in productive turbid waters using a Hyperspectral Imager for the Coastal Ocean\u2014The Azov Sea case study","volume":"6","author":"Gitelson","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2013.05.017","article-title":"Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model","volume":"136","author":"Song","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.rse.2015.11.003","article-title":"Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia","volume":"174","author":"Mueller","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0034-4257(00)00100-0","article-title":"Quantifying vegetation change in semiarid environments: Precision and accuracy of spectral mixture analysis and the normalized difference vegetation index","volume":"73","author":"Elmore","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.rse.2013.02.020","article-title":"Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis","volume":"133","author":"Liu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.14358\/PERS.70.9.1053","article-title":"Spectral mixture analysis of the urban landscape in indianapolis with Landsat ETM+ imagery","volume":"70","author":"Lu","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2005.01.002","article-title":"Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?","volume":"95","author":"Song","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_52","first-page":"44","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"Proc. IEEE"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/584\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:25:53Z","timestamp":1760210753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/584"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,12]]},"references-count":52,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2016,7]]}},"alternative-id":["rs8070584"],"URL":"https:\/\/doi.org\/10.3390\/rs8070584","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,12]]}}}