{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T13:53:35Z","timestamp":1772805215022,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1I1A3A04037483"],"award-info":[{"award-number":["NRF-2020R1I1A3A04037483"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cloud detection is an essential and important process in remote sensing when surface information is required for various fields. For this reason, we developed a daytime cloud detection algorithm for GEOstationary KOrea Multi-Purpose SATellite 2A (GEO-KOMPSAT-2A, GK-2A) imagery. For each pixel, the filtering technique using angular variance, which denotes the change in top of atmosphere (TOA) reflectance over time, was applied, and filtering technique by using the minimum TOA reflectance was used to remove remaining cloud pixels. Furthermore, near-infrared (NIR) and normalized difference vegetation index (NDVI) images were applied with dynamic thresholds to improve the accuracy of the cloud detection results. The quantitative results showed that the overall accuracy of proposed cloud detection was 0.88 and 0.92 with Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), respectively, and indicated that the proposed algorithm has good performance in detecting clouds.<\/jats:p>","DOI":"10.3390\/rs13163215","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T09:22:38Z","timestamp":1628846558000},"page":"3215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Daytime Cloud Detection Algorithm Based on a Multitemporal Dataset for GK-2A Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2322-9839","authenticated-orcid":false,"given":"Soobong","family":"Lee","sequence":"first","affiliation":[{"name":"National Meteorological Satellite Center, Korea Meteorological Administration, Jincheon 27803, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3967-6481","authenticated-orcid":false,"given":"Jaewan","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Chungbuk National University, Cheongju 28644, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s40747-019-00128-0","article-title":"Cloud detection methodologies: Variants and development\u2014A review","volume":"6","author":"Mahajan","year":"2020","journal-title":"Complex Intell. 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