{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:01:07Z","timestamp":1760241667761,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,2]],"date-time":"2018-08-02T00:00:00Z","timestamp":1533168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Province Natural Science Fund for Distinguished Young Scholars","award":["2018CFA062"],"award-info":[{"award-number":["2018CFA062"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.<\/jats:p>","DOI":"10.3390\/rs10081212","type":"journal-article","created":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T03:03:15Z","timestamp":1533265395000},"page":"1212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9352-7853","authenticated-orcid":false,"given":"Xiaohong","family":"Yang","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8285-8446","authenticated-orcid":false,"given":"Xiaodong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Yihang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Ming","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1080\/01431160010006881","article-title":"Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern zambia","volume":"22","author":"Petit","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Cao, Q., Zhao, J., Ma, A., Zhao, B., and Zhang, L. (2017). Optimal decision fusion for urban land-use\/land-cover classification based on adaptive differential evolution using hyperspectral and lidar data. Remote Sens., 9.","DOI":"10.3390\/rs9080868"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"637","DOI":"10.3390\/rs6010637","article-title":"Super-resolution reconstruction for multi-angle remote sensing images considering resolution differences","volume":"6","author":"Zhang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"839","DOI":"10.14358\/PERS.71.7.839","article-title":"Sub-pixel target mapping from soft-classified, remotely sensed imagery","volume":"71","author":"Atkinson","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1080\/01431160903131034","article-title":"Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study","volume":"30","author":"Atkinson","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/TGRS.2013.2244095","article-title":"Adaptive subpixel mapping based on a multiagent system for remote-sensing imagery","volume":"52","author":"Xu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","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."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1093\/comjnl\/bxm028","article-title":"Super-resolution reconstruction algorithm to modis remote sensing images","volume":"52","author":"Shen","year":"2008","journal-title":"Comput. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ge, Y., An, R., and Chen, Y. (2018). Super-resolution mapping of impervious surfaces from remotely sensed imagery with points-of-interest. Remote Sens., 10.","DOI":"10.3390\/rs10020242"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.14358\/PERS.73.11.1233","article-title":"Weighting function alternatives for a subpixel allocation model","volume":"73","author":"Makido","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1080\/01431160500207088","article-title":"Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixel-swapping","volume":"27","author":"Thornton","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1080\/01431161.2011.631607","article-title":"Super-resolution mapping based on the supervised fuzzy c-means approach","volume":"3","author":"Li","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_13","first-page":"1765","article-title":"Super resolution land cover mapping of satellite images using LDA based weighted FCM and hopfield neural network","volume":"6","author":"Genitha","year":"2013","journal-title":"Int. J. Earth Sci. Eng."},{"key":"ref_14","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. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1016\/j.isprsjprs.2011.08.002","article-title":"Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images","volume":"66","author":"Ardila","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TGRS.2014.2335818","article-title":"Improving the spatial resolution of landsat TM\/ETM + through fusion with SPOT5 images via learning-based super-resolution","volume":"53","author":"Song","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2003.06.002","article-title":"A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODIS: Application in the Brazilian amazon region","volume":"87","author":"Braswell","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"7131","DOI":"10.1080\/01431161.2010.519004","article-title":"Fractional forest cover mapping in the brazilian amazon with a combination of MODIS and TM images","volume":"32","author":"Lu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1080\/01431160500396741","article-title":"Localized soft classification for super-resolution mapping of the shoreline","volume":"27","author":"Muslim","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.04.054","article-title":"Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat","volume":"213","author":"Du","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5381","DOI":"10.1080\/01431160500213292","article-title":"Super-resolution mapping of the waterline from remotely sensed data","volume":"26","author":"Foody","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1080\/014311698214659","article-title":"Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution","volume":"19","author":"Foody","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1109\/TGRS.2005.861752","article-title":"Superresolution mapping using a Hopfield neural network with fused images","volume":"44","author":"Nguyen","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/LGRS.2005.851551","article-title":"Superresolution mapping using a Hopfield neural network with LIDAR data","volume":"2","author":"Nguyen","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1080\/01431160110053176","article-title":"Sub-pixel land cover mapping for per-field classification","volume":"22","author":"Aplin","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/JSTARS.2016.2533571","article-title":"Integrating object boundary in super-resolution land-cover mapping","volume":"10","author":"Chen","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2013.2268153","article-title":"A spatio\u2013temporal pixel-swapping algorithm for subpixel land cover mapping","volume":"11","author":"Xu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.rse.2017.05.011","article-title":"Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps","volume":"196","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1080\/01431169608948706","article-title":"Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data","volume":"17","author":"Foody","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.1080\/014311697216847","article-title":"Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels","volume":"18","author":"Bastin","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/LGRS.2013.2285404","article-title":"Unsupervised subpixel mapping of remotely sensed imagery based on fuzzy c-means clustering approach","volume":"11","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2014.03.012","article-title":"Enhancing MODIS land cover product with a spatial\u2013temporal modeling algorithm","volume":"147","author":"Cai","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Danielson, P., Yang, L., Jin, S., Homer, C., and Napton, D. (2016). An assessment of the cultivated cropland class of NLCD 2006 using a multi-source and multi-criteria approach. Remote Sens., 8.","DOI":"10.3390\/rs8020101"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.envsoft.2018.03.016","article-title":"Fuzzy definition of rural urban interface: An application based on land use change scenarios in Portugal","volume":"104","author":"Amato","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_38","first-page":"1","article-title":"Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods","volume":"7","author":"Vakhshoori","year":"2016","journal-title":"Geomatics"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.apgeog.2017.12.004","article-title":"Modelling the impact of urban growth on agriculture and natural land in Italy to 2030","volume":"91","author":"Martellozzo","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1080\/13658816.2015.1008004","article-title":"Multi-label class assignment in land-use modelling","volume":"29","author":"Omrani","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_41","first-page":"283","article-title":"Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based land transformation model: An integrated ML-CA-LTM modeling framework","volume":"54","author":"Omrani","year":"2017","journal-title":"Mapp. Sci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:16:10Z","timestamp":1760195770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,2]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081212"],"URL":"https:\/\/doi.org\/10.3390\/rs10081212","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,8,2]]}}}