{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T06:41:56Z","timestamp":1773729716623,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFC3104901"],"award-info":[{"award-number":["2022YFC3104901"]}]},{"name":"National Key R&amp;D Program of China","award":["GML2021GD0809"],"award-info":[{"award-number":["GML2021GD0809"]}]},{"name":"National Key R&amp;D Program of China","award":["2020HZBXDYW04024"],"award-info":[{"award-number":["2020HZBXDYW04024"]}]},{"name":"National Key R&amp;D Program of China","award":["41476157"],"award-info":[{"award-number":["41476157"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["2022YFC3104901"],"award-info":[{"award-number":["2022YFC3104901"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["GML2021GD0809"],"award-info":[{"award-number":["GML2021GD0809"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["2020HZBXDYW04024"],"award-info":[{"award-number":["2020HZBXDYW04024"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["41476157"],"award-info":[{"award-number":["41476157"]}]},{"name":"Daya Bay Smart Ocean Intelligent Platform Project","award":["2022YFC3104901"],"award-info":[{"award-number":["2022YFC3104901"]}]},{"name":"Daya Bay Smart Ocean Intelligent Platform Project","award":["GML2021GD0809"],"award-info":[{"award-number":["GML2021GD0809"]}]},{"name":"Daya Bay Smart Ocean Intelligent Platform Project","award":["2020HZBXDYW04024"],"award-info":[{"award-number":["2020HZBXDYW04024"]}]},{"name":"Daya Bay Smart Ocean Intelligent Platform Project","award":["41476157"],"award-info":[{"award-number":["41476157"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFC3104901"],"award-info":[{"award-number":["2022YFC3104901"]}]},{"name":"National Natural Science Foundation of China","award":["GML2021GD0809"],"award-info":[{"award-number":["GML2021GD0809"]}]},{"name":"National Natural Science Foundation of China","award":["2020HZBXDYW04024"],"award-info":[{"award-number":["2020HZBXDYW04024"]}]},{"name":"National Natural Science Foundation of China","award":["41476157"],"award-info":[{"award-number":["41476157"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8\/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg\/L in HZB) and decrease in summer (e.g., 6.83 mg\/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.<\/jats:p>","DOI":"10.3390\/rs16111951","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T06:58:07Z","timestamp":1716965887000},"page":"1951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8\/9 Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Lehua","family":"Dong","sequence":"first","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7747-3082","authenticated-orcid":false,"given":"Difeng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511400, China"},{"name":"Daya Bay Observation and Research Station of Marine Risks and Hazards, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"given":"Lili","family":"Song","sequence":"additional","affiliation":[{"name":"Marine Monitoring and Forecasting Center of Zhejiang Province, Hangzhou 310012, China"}]},{"given":"Fang","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"given":"Siyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Marine Monitoring and Forecasting Center of Zhejiang Province, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9789-8560","authenticated-orcid":false,"given":"Jingjing","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Ocean College, Zhejiang University, Hangzhou 316021, China"}]},{"given":"Xianqiang","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105701","DOI":"10.1016\/j.marenvres.2022.105701","article-title":"Ocean water quality monitoring using remote sensing techniques: A review","volume":"180","author":"Mohseni","year":"2022","journal-title":"Mar. 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