{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T03:50:37Z","timestamp":1782791437752,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971318"],"award-info":[{"award-number":["41971318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA19080304"],"award-info":[{"award-number":["XDA19080304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFB3901202"],"award-info":[{"award-number":["2021YFB3901202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["59193"],"award-info":[{"award-number":["59193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["41971318"],"award-info":[{"award-number":["41971318"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19080304"],"award-info":[{"award-number":["XDA19080304"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2021YFB3901202"],"award-info":[{"award-number":["2021YFB3901202"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["59193"],"award-info":[{"award-number":["59193"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["41971318"],"award-info":[{"award-number":["41971318"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["XDA19080304"],"award-info":[{"award-number":["XDA19080304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB3901202"],"award-info":[{"award-number":["2021YFB3901202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["59193"],"award-info":[{"award-number":["59193"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Dragon 5 Cooperation","award":["41971318"],"award-info":[{"award-number":["41971318"]}]},{"name":"Dragon 5 Cooperation","award":["XDA19080304"],"award-info":[{"award-number":["XDA19080304"]}]},{"name":"Dragon 5 Cooperation","award":["2021YFB3901202"],"award-info":[{"award-number":["2021YFB3901202"]}]},{"name":"Dragon 5 Cooperation","award":["59193"],"award-info":[{"award-number":["59193"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cyanobacterial harmful algal blooms (CyanoHABs) in inland water have emerged as a major global environmental challenge. Although satellite remote sensing technology has been widely used to monitor CyanoHABs, there are also some automatic extraction methods of CyanoHABs based on spectral indices (such as gradient mode, fixed threshold, and the Otsu method, etc.), the accuracy is generally not very high. This study developed a high-precision automatic extraction model for CyanoHABs using a deep learning (DL) network based on Sentinel-2 multi-spectral instrument (MSI) data of Chaohu Lake, China. First, we generated the CyanoHABs \u201cground truth\u201d dataset based on visual interpretation. Thereafter, we trained the CyanoHABs extraction model based on a DL image segmentation network (U-Net) and extracted CyanoHABs. Then, we compared three previous automatic CyanoHABs extraction methods based on spectral index threshold segmentation and evaluated the accuracy of the results. Based on \u201cground truth\u201d, at the pixel level, the F1 score and relative error (RE) of the DL model extraction results are 0.90 and 3%, respectively, which are better than that of the gradient mode (0.81,40%), the fixed threshold (0.81, 31%), and the Otsu method (0.53, 62%); at CyanoHABs area level, the R2 of the scatter fitting between DL model result and the \u201cground truth\u201d is 0.99, which is also higher than the other three methods (0.90, 0.92, 0.84, respectively). Finally, we produced the annual CyanoHABs frequency map based on DL model results. The frequency map showed that the CyanoHABs on the northwest bank are significantly higher than in the center and east of Chaohu Lake, and the most serious CyanoHABs occurred in 2018 and 2019. Furthermore, CyanoHAB extraction based on this model did not cause cloud misjudgment and exhibited good promotion ability in Taihu Lake, China. Hence, our findings indicate the high potential of the CyanoHABs extraction model based on DL in further high-precision and automatic extraction of CyanoHABs from large-scale water bodies.<\/jats:p>","DOI":"10.3390\/rs14194763","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T03:34:17Z","timestamp":1664163257000},"page":"4763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Deep Learning-Based Automatic Extraction of Cyanobacterial Blooms from Sentinel-2 MSI Satellite Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Kai","family":"Yan","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8590-9736","authenticated-orcid":false,"given":"Junsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People\u2019s Republic of China, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People\u2019s Republic of China, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3212-9584","authenticated-orcid":false,"given":"Danfeng","family":"Hong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunchang","family":"Mu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyao","family":"Yin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9628-1817","authenticated-orcid":false,"given":"Fangfang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenglei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, 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