{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:15:39Z","timestamp":1774120539226,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:00:00Z","timestamp":1721433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"East China Collaborative Innovation Fund for Meteorological Science and Technology","award":["QYHZ202110"],"award-info":[{"award-number":["QYHZ202110"]}]},{"name":"East China Collaborative Innovation Fund for Meteorological Science and Technology","award":["FY-APP-2022.0610"],"award-info":[{"award-number":["FY-APP-2022.0610"]}]},{"name":"Advanced Program for FY Satellite Applications 2022","award":["QYHZ202110"],"award-info":[{"award-number":["QYHZ202110"]}]},{"name":"Advanced Program for FY Satellite Applications 2022","award":["FY-APP-2022.0610"],"award-info":[{"award-number":["FY-APP-2022.0610"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named \u201cConvolutional and Attention-based Cloud Mask Net (CACM-Net)\u201d, was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) &lt; 70\u00b0 and &gt;70\u00b0, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA &lt; 70\u00b0 subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA &gt; 70\u00b0 section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January\u2013June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA &lt; 70\u00b0 and 7.8%, 11.3%, and 4.8% for SZA &gt; 70\u00b0, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results.<\/jats:p>","DOI":"10.3390\/rs16142660","type":"journal-article","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T12:20:38Z","timestamp":1721650838000},"page":"2660","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["CACM-Net: Daytime Cloud Mask for AGRI Onboard the FY-4A Satellite"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5385-5253","authenticated-orcid":false,"given":"Jingyuan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Marine Sciences and Technology, Zhejiang Ocean University, Zhoushan 316022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1167-3991","authenticated-orcid":false,"given":"Zhongfeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"SANYA Oceanographic Laboratory, Sanya 572000, China"}]},{"given":"Dongzhi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Biao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Jiayu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6986-7302","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Kuo","family":"Liao","sequence":"additional","affiliation":[{"name":"Fujian Meteorological Disaster Prevention Technology Center, Fuzhou 350007, China"}]},{"given":"Kailin","family":"Li","sequence":"additional","affiliation":[{"name":"Fujian Institute of Meteorological Sciences, Fuzhou 350007, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. 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