{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:25:17Z","timestamp":1775100317712,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000116197"],"award-info":[{"award-number":["4000116197"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Up-to-date information about the Earth\u2019s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover\/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover\/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps.<\/jats:p>","DOI":"10.3390\/rs12213523","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T09:22:45Z","timestamp":1603790565000},"page":"3523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":154,"title":["Automated Production of a Land Cover\/Use Map of Europe Based on Sentinel-2 Imagery"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9195-3330","authenticated-orcid":false,"given":"Radek","family":"Malinowski","sequence":"first","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"given":"Stanis\u0142aw","family":"Lewi\u0144ski","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4882-3239","authenticated-orcid":false,"given":"Marcin","family":"Rybicki","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"given":"Ewa","family":"Gromny","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1165-0503","authenticated-orcid":false,"given":"Ma\u0142gorzata","family":"Jenerowicz","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2207-3468","authenticated-orcid":false,"given":"Micha\u0142","family":"Krupi\u0144ski","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1353-3215","authenticated-orcid":false,"given":"Artur","family":"Nowakowski","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"given":"Cezary","family":"Wojtkowski","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"given":"Marcin","family":"Krupi\u0144ski","sequence":"additional","affiliation":[{"name":"Centrum Bada\u0144 Kosmicznych Polskiej Akademii Nauk (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland"}]},{"given":"Elke","family":"Kr\u00e4tzschmar","sequence":"additional","affiliation":[{"name":"Industrieanlagen-Betriebsgesellschaft MBH (IABG), Hermann-Reichelt-Str. 3, 01109 Dresden, Germany"}]},{"given":"Peter","family":"Schauer","sequence":"additional","affiliation":[{"name":"Industrieanlagen-Betriebsgesellschaft MBH (IABG), Hermann-Reichelt-Str. 3, 01109 Dresden, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.3390\/rs6053965","article-title":"Automated training sample extraction for global land cover mapping","volume":"6","author":"Radoux","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Latifovic, R., Pouliot, D., and Olthof, I. (2017). Circa 2010 land cover of Canada: Local optimization methodology and product development. Remote Sens., 9.","DOI":"10.3390\/rs9111098"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1175\/BAMS-D-13-00047.1","article-title":"The concept of essential climate variables in support of climate research, applications, and policy","volume":"95","author":"Bojinski","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1175\/BAMS-D-11-00254.1","article-title":"The ESA climate change initiative: Satellite data records for essential climate variables","volume":"94","author":"Hollmann","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.01.001","article-title":"Global land cover mapping using Earth observation satellite data: Recent progresses and challenges","volume":"103","author":"Ban","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"3567","DOI":"10.1080\/01431169408954345","article-title":"NDVI-derived land cover classifications at a global scale","volume":"15","author":"DeFries","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12070","DOI":"10.3390\/rs61212070","article-title":"Global land cover mapping: A review and uncertainty analysis","volume":"6","author":"Congalton","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.1080\/01431161.2014.930206","article-title":"Meta-discoveries from a synthesis of satellite-based land-cover mapping research","volume":"35","author":"Yu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","first-page":"1021","article-title":"An analysis of the IGBP global land-cover characterization process","volume":"65","author":"Loveland","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","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_16","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_17","first-page":"24","article-title":"GlobCover: The most detailed portrait of Earth","volume":"2008","author":"Arino","year":"2008","journal-title":"Eur. Sp. Agency Bull."},{"key":"ref_18","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_19","first-page":"30","article-title":"Next generation of global land cover characterization, mapping, and monitoring","volume":"25","author":"Giri","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","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":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1080\/17538947.2013.822574","article-title":"FROM-GC: 30 m global cropland extent derived through multisource data integration","volume":"6","author":"Yu","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/17538947.2012.713190","article-title":"Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges","volume":"5","author":"Townshend","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/17538947.2013.786146","article-title":"Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error","volume":"6","author":"Sexton","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/j.1466-8238.2010.00584.x","article-title":"Status and distribution of mangrove forests of the world using earth observation satellite data","volume":"20","author":"Giri","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-94-007-7969-3_5","article-title":"CORINE land cover and land cover change products","volume":"Volume 18","author":"Manakos","year":"2014","journal-title":"Land Use and Land Cover Mapping in Europe. Remote Sensing and Digital Image Processing"},{"key":"ref_26","unstructured":"(2020, April 22). HRL. Available online: https:\/\/land.copernicus.eu\/pan-european\/high-resolution-layers."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.09.006","article-title":"A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","volume":"146","author":"Yang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","first-page":"650","article-title":"Completion of the 1990s National Land Cover Data set for the conterminous United States from Landsat thematic mapper data and ancillary data sources","volume":"67","author":"Vogelmann","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., Zhu, Z., Xian, G., and Howard, D. (2019). Overall methodology design for the United States national land cover database 2016 products. Remote Sens., 11.","DOI":"10.3390\/rs11242971"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1080\/014311697218764","article-title":"An evaluation of some factors affecting the accuracy of classification by an artificial neural network","volume":"18","author":"Foody","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1016\/j.rse.2011.07.010","article-title":"Land cover classification with coarse spatial resolution data to derive continuous and discrete maps for complex regions","volume":"115","author":"Colditz","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Li, Q., Qiu, C., Ma, L., Schmitt, M., and Zhu, X.X. (2020). Mapping the land cover of Africa at 10 m resolution from multi-source remote sensing data with google earth engine. Remote Sens., 12.","DOI":"10.3390\/rs12040602"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1016\/j.rse.2009.02.004","article-title":"Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods","volume":"113","author":"Xian","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_36","first-page":"702","article-title":"Establishment of a 1-km pan-European land cover database for environmental monitoring","volume":"33","author":"Steinnocher","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_37","unstructured":"(2020, May 20). S2GLC. Available online: http:\/\/s2glc.cbk.waw.pl\/."},{"key":"ref_38","unstructured":"Nowakowski, A., Lewi\u0144ski, S., Rybicki, M., Malinowski, R., Gromny, E., Krupi\u0144ski, M., Kraetzschmar, E., Bielski, C., and Fernandez-Prieto, D. (2020). The use of low resolution databases for Sentinel-2 land cover classification. Eur. J. Remote Sens., under review."},{"key":"ref_39","unstructured":"(2020, April 21). CREODIAS. Available online: https:\/\/creodias.eu\/."},{"key":"ref_40","unstructured":"(2020, April 21). DIAS. Available online: https:\/\/www.copernicus.eu\/en\/access-data\/dias."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A calibration and products validation status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.1080\/01431161.2012.674230","article-title":"A global land-cover validation data set, part I: Fundamental design principles","volume":"33","author":"Olofsson","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ettehadi Osgouei, P., Kaya, S., Sertel, E., and Alganci, U. (2019). Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11030345"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Midekisa, A., Holl, F., Savory, D.J., Andrade-Pacheco, R., Gething, P.W., Bennett, A., and Sturrock, H.J.W. (2017). Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0184926"},{"key":"ref_46","unstructured":"Langanke, T. (2016). Copernicus Land Monitoring Service\u2014High Resolution Layer Imperviousness, Product Specifications Document, EEA. Copernicus Team at EEA."},{"key":"ref_47","unstructured":"Smith, G. (2018). GMES Initial Operations\/Copernicus Land monitoring services\u2014Validation of products Validation Services for the geospatial products of the Copernicus land Continental and local components HRL IMPERVIOUSNESS DEGREE 2015 VALIDATION REPORT."},{"key":"ref_48","unstructured":"Langanke, T. (2016). Copernicus Land Monitoring Service\u2014High Resolution Layer Grassland, Product Specifications Document, EEA. Copernicus Team at EEA."},{"key":"ref_49","unstructured":"Weirather, M., and Zeug, G. (2018). GMES Initial Operations\/Copernicus Land monitoring services\u2014Validation of products Validation Services for the geospatial products of the Copernicus land Continental and local components, HRL GRASSLAND 2015 VALIDATION REPORT."},{"key":"ref_50","unstructured":"Langanke, T., Moran, A., Dulleck, B., and Schleicher, C. (2017). Copernicus Land Monitoring Service\u2014High Resolution Layer Forest, Product Specifications Document, EEA. Copernicus Team at EEA."},{"key":"ref_51","unstructured":"Pennec, A. (2018). GMES Initial Operations\/Copernicus Land monitoring services\u2014Validation of products Validation Services for the geospatial products of the Copernicus land Continental and local components HRL FOREST 2015 FINAL VALIDATION REPORT."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tachikawa, T., Hato, M., Kaku, M., and Iwasaki, A. (2011, January 24\u201329). Characteristics of ASTER GDEM version 2. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6050017"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.isprsjprs.2012.01.005","article-title":"Oil spill feature selection and classification using decision tree forest on SAR image data","volume":"68","author":"Topouzelis","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9010095"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.ecolind.2015.03.037","article-title":"Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI\/TIRS imageries: A comparative assessment of various spectral indices","volume":"56","author":"Estoque","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1080\/01431160600784259","article-title":"Land cover classification using multi-temporal MERIS vegetation indices","volume":"28","author":"Dash","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lewi\u0144ski, S., Nowakowski, A., Malinowski, R., Rybicki, M., Kukawska, E., and Krupi\u0144ski, M. (2017, January 4). Aggregation of Sentinel-2 Time Series Classifications as a Solution for Multitemporal Analysis. Proceedings of the SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland. 104270B.","DOI":"10.1117\/12.2277976"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","article-title":"Ensemble based systems in decision making","volume":"6","author":"Polikar","year":"2006","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kukawska, E., Lewinski, S., Krupinski, M., Malinowski, R., Nowakowski, A., Rybicki, M., and Kotarba, A. (2017, January 27\u201329). Multitemporal Sentinel-2 data\u2013Remarks and observations. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035212"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of copernicus Sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and FMask processors using reference cloud masks generated with a supervised active learning procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1080\/2150704X.2016.1249299","article-title":"A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data","volume":"8","author":"Mack","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_67","unstructured":"Heymann, Y., Steenmans, C., Croisille, G., Bossard, M., Lenco, M., Wyatt, B., Weber, J.-L., O\u2019Brian, C., Cornaert, M.-H., and Sifakis, N. (1994). CORINE Land Cover: Technical Guide. Environment, Nuclear Safety and Civil Protection Series, Commission of the European Communities, Office for Official Publications of the European Communities."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.gloplacha.2006.07.007","article-title":"Recent glacier changes in the Alps observed by satellite: Consequences for future monitoring strategies","volume":"56","author":"Paul","year":"2007","journal-title":"Glob. Planet. Chang."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.accre.2020.03.003","article-title":"Regional differences in global glacier retreat from 1980 to 2015","volume":"10","author":"Li","year":"2019","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"5851","DOI":"10.1080\/01431161.2013.798055","article-title":"Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach","volume":"34","author":"Yu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2002.06.006","article-title":"Approaches to fractional land cover and continuous field mapping: A comparative assessment over the BOREAS study region","volume":"89","author":"Fernandes","year":"2004","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3523\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:29:19Z","timestamp":1760178559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,27]]},"references-count":72,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213523"],"URL":"https:\/\/doi.org\/10.3390\/rs12213523","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,27]]}}}