{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T20:50:47Z","timestamp":1770151847691,"version":"3.49.0"},"reference-count":95,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry for Innovation and Technology (Hungary) - New National Excellence Program","award":["\u00daNKP-19-3-III-DE-94"],"award-info":[{"award-number":["\u00daNKP-19-3-III-DE-94"]}]},{"name":"Ministry for Innovation and Technology (Hungary) - Thematic Excellence Program","award":["TKP2020-NKA-04"],"award-info":[{"award-number":["TKP2020-NKA-04"]}]},{"DOI":"10.13039\/501100012550","name":"Nemzeti Kutat\u00e1si, Fejleszt\u00e9si \u00e9s Innovaci\u00f3s Alap","doi-asserted-by":"publisher","award":["TNN 123457"],"award-info":[{"award-number":["TNN 123457"]}],"id":[{"id":"10.13039\/501100012550","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km \u00d7 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA\u2019s accuracy, and PlanetScope\u2019s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1\u201378.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.<\/jats:p>","DOI":"10.3390\/rs13050857","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T04:36:24Z","timestamp":1614314184000},"page":"857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2060-945X","authenticated-orcid":false,"given":"Orsolya Gy\u00f6ngyi","family":"Varga","sequence":"first","affiliation":[{"name":"Department of Physical Geography and Geoinformation Systems, Doctoral School of Earth Sciences, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"},{"name":"Envirosense Hungary Ltd., 4281 L\u00e9tav\u00e9rtes, Hungary"}]},{"given":"Zolt\u00e1n","family":"Kov\u00e1cs","sequence":"additional","affiliation":[{"name":"Envirosense Hungary Ltd., 4281 L\u00e9tav\u00e9rtes, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2956-6193","authenticated-orcid":false,"given":"L\u00e1szl\u00f3","family":"Bek\u0151","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, University of Debrecen, B\u00f6sz\u00f6rm\u00e9nyi \u00fat 138, 4032 Debrecen, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5989-5521","authenticated-orcid":false,"given":"P\u00e9ter","family":"Burai","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, University of Debrecen, B\u00f6sz\u00f6rm\u00e9nyi \u00fat 138, 4032 Debrecen, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0668-2474","authenticated-orcid":false,"given":"Zsuzsanna","family":"Csat\u00e1rin\u00e9 Szab\u00f3","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Geoinformation Systems, Faculty of Science and Technology, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-4660","authenticated-orcid":false,"given":"Imre","family":"Holb","sequence":"additional","affiliation":[{"name":"Institute of Horticulture, University of Debrecen, B\u00f6sz\u00f6rm\u00e9nyi \u00fat 138, 4032 Debrecen, Hungary"},{"name":"E\u00f6tv\u00f6s Lor\u00e1nd Research Network (ELKH), Centre for Agricultural Research, Plant Protection Institute, Herman Ott\u00f3 \u00fat 15, 1022 Budapest, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8381-7942","authenticated-orcid":false,"given":"Sarawut","family":"Ninsawat","sequence":"additional","affiliation":[{"name":"Asian Institute of Technology (AIT), Remote Sensing and Geographic Information Systems (RS&amp;GIS) FoS, Klong Luang, Pathumthani 12120, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2670-7384","authenticated-orcid":false,"given":"Szil\u00e1rd","family":"Szab\u00f3","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Geoinformation Systems, Faculty of Science and Technology, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5215","DOI":"10.1080\/01431161.2017.1325529","article-title":"Heterogeneous Forest Classification by Creating Mixed Vegetation Classes using EO-1 Hyperion","volume":"38","author":"Telbisz","year":"2017","journal-title":"Int. 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