{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T02:41:43Z","timestamp":1780454503021,"version":"3.54.1"},"reference-count":89,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2242212"],"award-info":[{"award-number":["U2242212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFC3004100"],"award-info":[{"award-number":["2022YFC3004100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFC3004102"],"award-info":[{"award-number":["2022YFC3004102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["U2242212"],"award-info":[{"award-number":["U2242212"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFC3004100"],"award-info":[{"award-number":["2022YFC3004100"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFC3004102"],"award-info":[{"award-number":["2022YFC3004102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cloud contamination is a critical source of errors in the data assimilation of hyperspectral infrared radiance (IR). Therefore, it is necessary to filter out cloudy observations. In this study, we review and summarize the principles and research progress of cloud detection methods for the hyperspectral IR in the past two decades. Based on the impact of IR data utilization on cloud detection results, cloud detection methods are categorized into five types, namely clear field-of-view (FOV) detection, clear channel detection, three-dimensional cloud detection, cloud-clearing and deep learning methods. Clear FOV methods and clear channel methods aim to identify the purely clear FOVs and spectral channels that are not affected by clouds, respectively. Cloud-clearing methods are used to reconstruct clear-column radiance for cloudy observations. Deep learning cloud detection methods can quickly learn the mapping relationship between infrared hyperspectral radiation characteristics and FOV cloud distribution from a large amount of infrared radiative information with known FOV cloud labels. In this paper, we discuss and provide an outlook on the key issues in current hyperspectral IR cloud detection. Specifically, we analyze and summarize the factors affecting cloud detection, such as surface background information, vertical cloud distribution, hyperspectral IR channel selection, improvements in cloud detection algorithms and model applicability. The results indicate the use of deep learning methods offer advantages in detection accuracy and algorithm efficiency of hyperspectral IR cloud detection.<\/jats:p>","DOI":"10.3390\/rs16244629","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T12:22:06Z","timestamp":1733833326000},"page":"4629","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-1391","authenticated-orcid":false,"given":"Zhuoya","family":"Ni","sequence":"first","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"Key Laboratory of Transporation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"},{"name":"State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengdie","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Earth Science and Resources, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Lu","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5843-3106","authenticated-orcid":false,"given":"Hongyuan","family":"Huo","sequence":"additional","affiliation":[{"name":"Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7409-8712","authenticated-orcid":false,"given":"Chunqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixia","family":"Liu","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu","family":"Wang","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Xu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Meteorological Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s13351-016-5114-2","article-title":"On the assimilation of satellite sounder data in cloudy skies in numerical weather prediction models","volume":"30","author":"Li","year":"2016","journal-title":"J. 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