{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T21:17:18Z","timestamp":1784409438864,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T00:00:00Z","timestamp":1603324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19090200"],"award-info":[{"award-number":["XDA19090200"]}]},{"name":"the Key Research Program of the Chinese Academy of Sciences","award":["ZDRW-ZS-2019-1"],"award-info":[{"award-number":["ZDRW-ZS-2019-1"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41825002"],"award-info":[{"award-number":["41825002"]}],"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>Land-cover plays an important role in the Earth\u2019s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use\/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 \u00b1 0.68%; this was followed by GLC_FCS30-2015 (84.33 \u00b1 0.80%) and FROM_GLC2015 (65.31 \u00b1 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.<\/jats:p>","DOI":"10.3390\/rs12213479","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T20:51:00Z","timestamp":1603399860000},"page":"3479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset"],"prefix":"10.3390","volume":"12","author":[{"given":"Yuan","family":"Gao","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-037X","authenticated-orcid":false,"given":"Liangyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xidong","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Mi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2012.09.006","article-title":"Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe","volume":"74","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.rse.2018.12.001","article-title":"Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey","volume":"221","author":"Pflugmacher","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/20964471.2019.1663627","article-title":"A global land cover map produced through integrating multi-source datasets","volume":"3","author":"Feng","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1080\/014311600210209","article-title":"Global land cover classification at 1 km spatial resolution using a classification tree approach","volume":"21","author":"Hansen","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1080\/01431160412331291297","article-title":"GLC2000: A new approach to global land cover mapping from Earth observation data","volume":"26","author":"Belward","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0034-4257(02)00078-0","article-title":"Global land cover mapping from MODIS: Algorithms and early results","volume":"83","author":"Friedl","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_10","unstructured":"Bicheron, P., Leroy, M., Brockmann, C., Kr\u00e4mer, U., Miras, B., Huc, M., Ni\u00f1o, F., Defourny, P., Vancutsem, C., and Arino, O. (2006, January 25\u201329). Globcover: A 300 m global land cover product for 2005 using ENVISAT MERIS time series. Proceedings of the Second International Symposium on Recent Advances in Quantitative Remote Sensing, Torrent, Valencia, Spain."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lamarche, C., Santoro, M., Bontemps, S., D\u2019Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P., and Arino, O. (2017). Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland\/Ocean Water Tailored to the Climate Modeling Community. Remote Sens., 9.","DOI":"10.3390\/rs9010036"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1016\/j.rse.2007.11.013","article-title":"Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets","volume":"112","author":"Herold","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"207","article-title":"Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale","volume":"13","author":"Roujean","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"1","article-title":"GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery","volume":"2020","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1016\/j.rse.2011.08.016","article-title":"Comparison and assessment of coarse resolution land cover maps for Northern Eurasia","volume":"115","author":"Pflugmacher","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.isprsjprs.2017.01.016","article-title":"Accuracy assessment of seven global land cover datasets over China","volume":"125","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kang, J., Wang, Z., Sui, L., Yang, X., Ma, Y., and Wang, J. (2020). Consistency Analysis of Remote Sensing Land Cover Products in the Tropical Rainforest Climate Region: A Case Study of Indonesia. Remote Sens., 12.","DOI":"10.3390\/rs12091410"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kang, J., Sui, L., Yang, X., Wang, Z., Huang, C., and Wang, J. (2019). Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8050201"},{"key":"ref_21","first-page":"124","article-title":"Comparative assessment of thematic accuracy of GLC maps for specific applications using existing reference data","volume":"44","author":"Tsendbazar","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.5194\/essd-12-1625-2020","article-title":"Development of a global 30\u2009m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform","volume":"12","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8739","DOI":"10.3390\/rs6098739","article-title":"Assessing Consistency of Five Global Land Cover Data Sets in China","volume":"6","author":"Bai","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2014.02.008","article-title":"Assessing global land cover reference datasets for different user communities","volume":"103","author":"Tsendbazar","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1080\/01431161.2014.930202","article-title":"Towards a common validation sample set for global land-cover mapping","volume":"35","author":"Zhao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.rse.2018.10.025","article-title":"Developing and applying a multi-purpose land cover validation dataset for Africa","volume":"219","author":"Tsendbazar","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"170136","DOI":"10.1038\/sdata.2017.136","article-title":"A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform","volume":"4","author":"Bayas","year":"2017","journal-title":"Sci. Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"15804","DOI":"10.3390\/rs71215804","article-title":"Spatial Accuracy Assessment and Integration of Global Land Cover Datasets","volume":"7","author":"Tsendbazar","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","unstructured":"Collaboration in Research and Methodology for Official Statistics (CROS) (2020, July 13). Methodology for Data Validation 2.0. Handbook of the European Statistical System (ESS). Available online: https:\/\/ec.europa.eu\/eurostat\/cros\/system\/files\/methodology_for_data_validation_v1.0_rev-2016-06_final.pdf."},{"key":"ref_30","first-page":"431","article-title":"Using Known Map Category Marginal Frequencies to Improve Estimates of Thematic Map Accuracy","volume":"48","author":"Card","year":"1982","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"European Environmental Agency (EEA) (2020, July 13). Biogeographical Regions. Available online: https:\/\/www.eea.europa.eu\/data-and-maps\/data\/biogeographical-regions-europe-3."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3659","DOI":"10.1080\/01431161.2015.1047049","article-title":"Adaptively weighted decision fusion in 30 m land-cover mapping with Landsat and MODIS data","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","first-page":"467","article-title":"Using CORINE land cover and the point survey LUCAS for area estimation","volume":"10","author":"Gallego","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1111\/ejss.12499","article-title":"LUCAS Soil, the largest expandable soil dataset for Europe: A review","volume":"69","author":"Orgiazzi","year":"2017","journal-title":"Eur. J. Soil Sci."},{"key":"ref_36","first-page":"102065","article-title":"Spatial and semantic effects of LUCAS samples on fully automated land use\/land cover classification in high-resolution Sentinel-2 data","volume":"88","author":"Weigand","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.scitotenv.2014.02.010","article-title":"Soil erodibility in Europe: A high-resolution dataset based on LUCAS","volume":"479-480","author":"Panagos","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5012","DOI":"10.3390\/rs70505012","article-title":"Use of LUCAS LC Point Database for Validating Country-Scale Land Cover Maps","volume":"7","author":"Karydas","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","unstructured":"Defourny, P., Kirches, G., Brockmann, C., Boettcher, M., Peters, M., Bontemps, S., Lamarche, C., Schlerf, M., and Santoro, M. (2016). Land Cover CCI: Product User Guide Version 2, European Space Agency (ESA)."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_41","unstructured":"Arnold, S., Kosztra, B., Banko, G., Smith, G., Hazeu, G.W., Bock, M., and Valcarcel, N. (2013, January 3\u20136). The EAGLE Concept\u2014A Vision of a Future European Land Monitoring Framework. Proceedings of the 33rd EARSeL symposium\u201cTowards Horizon 2020\u201d, Matera, Italy."},{"key":"ref_42","unstructured":"Mozak, S. (2016). Comparing Global Land Cover Datasets through the Eagle Matrix Land Cover Components for Continental Portugal, James I University."},{"key":"ref_43","first-page":"246","article-title":"A spatial comparison of four satellite derived 1 km global land cover datasets","volume":"8","author":"McCallum","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","first-page":"427","article-title":"Consistency of Land Cover Data Derived from Remote Sensing in Xinjiang","volume":"21","author":"Xu","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_46","unstructured":"Strandell, H., and Wolff, P. (2020). Environment and natural resources. The EU in the World, Eurostat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1109\/TGRS.2006.864370","article-title":"Validation of the global land cover 2000 map","volume":"44","author":"Mayaux","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:26:14Z","timestamp":1760178374000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,22]]},"references-count":47,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213479"],"URL":"https:\/\/doi.org\/10.3390\/rs12213479","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,22]]}}}