{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T13:11:16Z","timestamp":1768741876306,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences","award":["No. GBL12107"],"award-info":[{"award-number":["No. GBL12107"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.42001308, No.U21A2013 and No.41925007"],"award-info":[{"award-number":["No.42001308, No.U21A2013 and No.41925007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water body mapping is an effective way to monitor dynamic changes in surface water, which is of great significance for water resource management. Super-resolution mapping is a valid method to generate high-resolution dynamic water body maps from low-spatial-resolution images. However, the accuracy of existing super-resolution mapping methods is not high due to the low accuracy of fraction images and the insufficiency of spatial pattern information. To solve this problem, this paper proposes a spectral similarity scale-based multiple-endmember spectral mixture analysis (SSS-based MESMA) and a multiscale spatio-temporal dependence method based on super-resolution mapping (MESMA_MST_SRM) for water bodies. SSS-based MESMA allows different coarse pixels to have different endmember combinations, which can effectively improve the accuracy of spectral unmixing and then improve the accuracy of fraction images. Multiscale spatio-temporal dependence adopts both pixel-based and subpixel-based spatial dependence. In this study, eight different types of water body mappings derived from the Landsat 8 Operational Land Imager (OLI) and Google Earth images were employed to test the performance of the MESMA_MST_SRM method. The results of the eight experiments showed that compared with the other four tested methods, the overall accuracy (OA) value, as well as the overall distribution and detailed information of the water map generated by the MESMA_MST_SRM method, were the best, indicating the great potential and efficiency of the proposed method in water body mapping.<\/jats:p>","DOI":"10.3390\/rs14092050","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T02:14:39Z","timestamp":1650939279000},"page":"2050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Water Body Super-Resolution Mapping Based on Multiple Endmember Spectral Mixture Analysis and Multiscale Spatio-Temporal Dependence"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9352-7853","authenticated-orcid":false,"given":"Xiaohong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Qiannian","family":"Chu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-0845","authenticated-orcid":false,"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Menghui","family":"Yu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1038\/s41561-018-0265-7","article-title":"Recent global decline in endorheic basin water storages","volume":"11","author":"Wang","year":"2018","journal-title":"Nat. Geosci."},{"key":"ref_2","first-page":"1","article-title":"Gainers and losers of surface and terrestrial water resources in China during 1989\u20132016","volume":"11","author":"Wang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tzanakakis, V.A., Paranychianakis, N.V., and Angelakis, A.N.J.W. (2020). Water supply and water scarcity. Water, 12.","DOI":"10.3390\/w12092347"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112028","DOI":"10.1016\/j.rse.2020.112028","article-title":"Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index","volume":"250","author":"Xu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s12403-015-0170-x","article-title":"Evaluation of shallow groundwater contamination and associated human health risk in an alluvial plain impacted by agricultural and industrial activities, mid-west China","volume":"8","author":"Wu","year":"2015","journal-title":"Expo. Health"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Loucks, D.P., and van Beek, E. (2017). Water Resources Planning and Management: An Overview. Water Resource Systems Planning and Management, Springer.","DOI":"10.1007\/978-3-319-44234-1"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/S0034-4257(02)00059-7","article-title":"Waterline extraction from Landsat TM data in a tidal flat: A case study in Gomso Bay, Korea","volume":"83","author":"Ryu","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.envsci.2004.05.002","article-title":"The role of remote sensing technology in the EU water framework directive (WFD)","volume":"7","author":"Chen","year":"2004","journal-title":"Environ. Sci. Policy."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.4319\/lo.2009.54.6_part_2.2273","article-title":"Lakes and reservoirs as sentinels, integrators, and regulators of climate change","volume":"54","author":"Williamson","year":"2009","journal-title":"Limnol. Oceanogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.ecolind.2014.06.035","article-title":"The potential of remote sensing in ecological status assessment of coloured lakes using aquatic plants","volume":"46","author":"Birk","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2014.07.119","article-title":"Assessing the ecological status in the context of the European Water Framework Directive: Where do we go now?","volume":"497\u2013498","author":"Reyjol","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Subedi, A., and Lee, D.H. (2018). Evaluation of water indices for surface water extraction in a Landsat 8 scene of Nepal. Sensors, 18.","DOI":"10.3390\/s18082580"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the 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_15","doi-asserted-by":"crossref","first-page":"12336","DOI":"10.3390\/rs70912336","article-title":"High-resolution mapping of urban surface water using ZY-3 multi-spectral imagery","volume":"7","author":"Yao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Lee, D.H., Yang, I.T., and Lee, J.K. (2016). Identification of water bodies in a Landsat 8 OLI image using a J48 decision tree. Sensors, 16.","DOI":"10.3390\/s16071075"},{"key":"ref_17","first-page":"226","article-title":"A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques","volume":"34","author":"Rokni","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.rse.2013.10.008","article-title":"A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data","volume":"140","author":"Pekel","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, Y., Huang, S., Li, J., Li, X., Ma, J., Li, S., and Wang, H. (2015, January 9). Dynamic monitoring of Poyang Lake water body area using MODIS images between 2000 and 2014. Proceedings of the International Conference on Intelligent Earth Observing and Applications, Guilin, China.","DOI":"10.1117\/12.2209293"},{"key":"ref_20","first-page":"387","article-title":"Estimating and Validating Wheat Leaf Water Content with Three MODIS Spectral Indexes: A Case Study in Ningx ia Plain, China","volume":"18","author":"Zhao","year":"2018","journal-title":"J. Agric. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rao, P., Jiang, W., Hou, Y., Chen, Z., and Jia, K. (2018). Dynamic change analysis of surface water in the Yangtze River Basin based on MODIS products. Remote Sens., 10.","DOI":"10.3390\/rs10071025"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.034","article-title":"Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region","volume":"178","author":"Tulbure","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1073\/pnas.1411748112","article-title":"Rapid loss of lakes on the Mongolian Plateau","volume":"112","author":"Tao","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/17538947.2015.1026420","article-title":"A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic\u2013spectral classification algorithm","volume":"9","author":"Feng","year":"2016","journal-title":"Int. J. Digit. Earth."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2015.10.014","article-title":"Development of a global~90 m water body map using multi-temporal Landsat images","volume":"171","author":"Yamazaki","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xia, H., Zhao, J., Qin, Y., Yang, J., Cui, Y., Song, H., Ma, L., Jin, N., and Meng, Q. (2019). Changes in water surface area during 1989\u20132017 in the Huai River Basin using Landsat data and Google earth engine. Remote Sens., 11.","DOI":"10.3390\/rs11151824"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"W09504","DOI":"10.1029\/2012WR012063","article-title":"Global monitoring of large reservoir storage from satellite remote sensing","volume":"48","author":"Gao","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/01431169408954100","article-title":"Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions","volume":"15","author":"Foody","year":"1993","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5631","DOI":"10.1029\/2018WR024136","article-title":"Measuring river wetted width from remotely sensed imagery at the subpixel scale with a deep convolutional neural network","volume":"55","author":"Ling","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.rse.2005.02.006","article-title":"Super-resolution land cover mapping using a Markov random field based approach","volume":"96","author":"Kasetkasem","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5023","DOI":"10.1080\/01431160903252350","article-title":"Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images","volume":"31","author":"Ling","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4951","DOI":"10.1109\/TGRS.2019.2894773","article-title":"Spatial-temporal super-resolution land cover mapping with a local spatial-temporal dependence model","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1109\/JSTARS.2014.2320256","article-title":"Super-resolution land cover mapping with spatial\u2013temporal dependence by integrating a former fine resolution map","volume":"7","author":"Ling","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/JSTARS.2013.2264828","article-title":"Super-resolution mapping of forests with bitemporal different spatial resolution images based on the spatial-temporal Markov random field","volume":"7","author":"Li","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2015.04.009","article-title":"Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm","volume":"164","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1080\/01431160701802489","article-title":"Waterline mapping at the subpixel scale from remote sensing imagery with high-resolution digital elevation models","volume":"29","author":"Ling","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TGRS.2016.2613140","article-title":"Learning-based spatial\u2013temporal super-resolution mapping of forest cover with modis images","volume":"55","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.isprsjprs.2020.08.008","article-title":"Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information","volume":"168","author":"Ling","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., Wei, Y., Chen, Z., and Xie, P. (2020). Water Body Extraction from Sentinel-3 Image with Multiscale Spatiotemporal Super-Resolution Mapping. Water, 12.","DOI":"10.3390\/w12092605"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1175\/JHM-D-19-0021.1","article-title":"Improving hydrologic modeling using cloud-free MODIS flood maps","volume":"20","author":"Tran","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1080\/01431161.2011.608091","article-title":"Development of a sub-pixel analysis method applied to dynamic monitoring of floods","volume":"33","author":"Osorio","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3293","DOI":"10.1080\/01431160500497127","article-title":"A sub-pixel mapping algorithm based on sub-pixel\/pixel spatial attraction models","volume":"27","author":"Mertens","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2831","DOI":"10.1080\/01431161.2015.1047048","article-title":"Spectral\u2013spatial based sub-pixel mapping of remotely sensed imagery with multi-scale spatial dependence","volume":"36","author":"Zhang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","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_46","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_47","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2016.06.015","article-title":"Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems","volume":"184","author":"Quintano","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_48","first-page":"290","article-title":"Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters","volume":"33","author":"Fan","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","first-page":"245","article-title":"A new approach to inventorying bodies of water, from local to global scale","volume":"146","author":"Bartout","year":"2015","journal-title":"Erde"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"9","DOI":"10.23818\/limn.29.02","article-title":"Emerging global role of small lakes and ponds: Little things mean a lot","volume":"29","author":"Downing","year":"2009","journal-title":"Limnetica."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1139\/F08-110","article-title":"A preliminary national analysis of some key characteristics of Canadian lakes","volume":"65","author":"Minns","year":"2008","journal-title":"Can. J. Fish. Aquat. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.4319\/lo.2006.51.5.2388","article-title":"The global abundance and size distribution of lakes, ponds, and impoundments","volume":"51","author":"Downing","year":"2006","journal-title":"Limnol. Oceanogr."},{"key":"ref_53","unstructured":"Granahan, J., and Sweet, J. (2001, January 9\u201313). An evaluation of atmospheric correction techniques using the spectral similarity scale. Proceedings of the International Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","article-title":"Super-resolution image reconstruction: A technical overview","volume":"20","author":"Park","year":"2003","journal-title":"IEEE Signal Process. Mag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2050\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:00:30Z","timestamp":1760137230000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092050"],"URL":"https:\/\/doi.org\/10.3390\/rs14092050","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,25]]}}}