{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:46:56Z","timestamp":1772822816582,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["(2016YFC1400901)"],"award-info":[{"award-number":["(2016YFC1400901)"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the High-Resolution Earth Observation Systems of National Science and Technology Major Projects","award":["(41-Y20A31\u20139003\u201315\/17)"],"award-info":[{"award-number":["(41-Y20A31\u20139003\u201315\/17)"]}]},{"name":"Special Project for Team Building of Henan Academy of Sciences","award":["(200501007)"],"award-info":[{"award-number":["(200501007)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds that exert a significant impact on the radiation budget of planet earth. Although the traditional cloud detection methods have generally performed well, these methods were usually developed specifically for particular sensors in a particular region with a particular underlying surface (e.g., land, water, vegetation, and man-made objects). Coastal regions are known to have a variety of underlying surfaces, which represent a major challenge in cloud detection. Therefore, there is an urgent requirement for developing a cloud detection method that could be applied to a variety of sensors, situations, and underlying surfaces. In the present study, a cloud detection method based on spatial and spectral uniformity of clouds was developed. In addition to having a spatially uniform texture, a spectrally approximate value was also present between the blue and green bands of the cloud region. The blue and green channel data appeared more uniform over the cloudy region, i.e., the entropy of the cloudy region was lower than that of the cloud-free region. On the basis of this difference in entropy, it would be possible to categorize the satellite images into cloud region images and cloud-free region images. Furthermore, the performance of the proposed method was validated by applying it to the data from various sensors across the coastal zone of the South China Sea. The experimental results demonstrated that compared to the existing operational algorithms, EN-clustering exhibited higher accuracy and scalability, and also performed robustly regardless of the spatial resolution of the different satellite images. It is concluded that the EN-clustering algorithm proposed in the present study is applicable to different sensors, different underlying surfaces, and different regions, with the support of NDSI and NDBI indices to remove the interference information from snow, ice, and man-made objects.<\/jats:p>","DOI":"10.3390\/rs12183003","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T10:24:09Z","timestamp":1600165449000},"page":"3003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Effective Method for Detecting Clouds in GaoFen-4 Images of Coastal Zones"],"prefix":"10.3390","volume":"12","author":[{"given":"Zheng","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Geographical Science, Henan Academy of Science, Zhengzhou 450052, China"},{"name":"States Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"given":"Jun","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Geographical Science, Henan Academy of Science, Zhengzhou 450052, China"}]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[{"name":"Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan"}]},{"given":"Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}]},{"given":"Qun","family":"Zeng","sequence":"additional","affiliation":[{"name":"The College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"},{"name":"Editorial Department of Journal, Central China Normal University, Wuhan 430079, China"}]},{"given":"Liqiao","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Lihui","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0066-1808","authenticated-orcid":false,"given":"Zhihua","family":"Mao","sequence":"additional","affiliation":[{"name":"States Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Rossow, W.B., Lacis, A.A., Oinas, V., and Mishchenko, M.I. 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