Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea
Chlorophyll-a (Chl-a) plays a vital role in assessing environmental health and understanding the response of marine ecosystems to physical factors and climate change. In situ sampling, remote sensing, and moored buoys or floats are commonly employed methods for obtaining Chl-a in marine science rese...
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| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1528921/full |
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| author | Weiwei Fang Ao Li Ao Li Haoyu Jiang Haoyu Jiang Chan Shu Peng Xiu |
| author_facet | Weiwei Fang Ao Li Ao Li Haoyu Jiang Haoyu Jiang Chan Shu Peng Xiu |
| author_sort | Weiwei Fang |
| collection | DOAJ |
| description | Chlorophyll-a (Chl-a) plays a vital role in assessing environmental health and understanding the response of marine ecosystems to physical factors and climate change. In situ sampling, remote sensing, and moored buoys or floats are commonly employed methods for obtaining Chl-a in marine science research. Although in situ sampling, buoys, and floats could provide accurate data, they are limited by the spatial and temporal resolution. Remote sensing offers continuous and broad spatial coverage, while it is often hindered by cloud cover in the South China Sea (SCS). This study discussed the feasibility of a predictive model by linking the physical factors [e.g., wind field, surface currents, sea surface height (SSH), and sea surface temperature (SST)] with surface Chl-a in the SCS based on the ResUnet. The ResUnet architecture performs well in capturing non-linear relationships between variables, with the model achieving a prediction accuracy exceeding 90%. The results indicate that (1) the combination of oceanic dynamical and meteorological data could effectively estimate the Chl-a based on deep learning methods; (2) the combination of meteorological and SST effectively reproduces Chl-a in the northern SCS, while adding surface currents and SSH improves model performance in the southern SCS; (3) With the addition of surface currents and SSH, the model effectively captures the high Chl-a patches induced by eddies. This research presents a viable method for estimating surface Chl-a concentrations in regions where they are highly correlated with dynamic factors, using deep learning and comprehensive oceanic and atmospheric data. |
| format | Article |
| id | doaj-art-409b6a7cea03489f87c8b490054ee977 |
| institution | DOAJ |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-409b6a7cea03489f87c8b490054ee9772025-08-20T03:00:55ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-03-011210.3389/fmars.2025.15289211528921Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China SeaWeiwei Fang0Ao Li1Ao Li2Haoyu Jiang3Haoyu Jiang4Chan Shu5Peng Xiu6State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan, ChinaShenzhen Research Institute, China University of Geosciences, Shenzhen, ChinaShenzhen Research Institute, China University of Geosciences, Shenzhen, ChinaCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, ChinaCollege of Mathematics and Statistics, Huanggang Normal University, Huanggang, ChinaState Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen, ChinaChlorophyll-a (Chl-a) plays a vital role in assessing environmental health and understanding the response of marine ecosystems to physical factors and climate change. In situ sampling, remote sensing, and moored buoys or floats are commonly employed methods for obtaining Chl-a in marine science research. Although in situ sampling, buoys, and floats could provide accurate data, they are limited by the spatial and temporal resolution. Remote sensing offers continuous and broad spatial coverage, while it is often hindered by cloud cover in the South China Sea (SCS). This study discussed the feasibility of a predictive model by linking the physical factors [e.g., wind field, surface currents, sea surface height (SSH), and sea surface temperature (SST)] with surface Chl-a in the SCS based on the ResUnet. The ResUnet architecture performs well in capturing non-linear relationships between variables, with the model achieving a prediction accuracy exceeding 90%. The results indicate that (1) the combination of oceanic dynamical and meteorological data could effectively estimate the Chl-a based on deep learning methods; (2) the combination of meteorological and SST effectively reproduces Chl-a in the northern SCS, while adding surface currents and SSH improves model performance in the southern SCS; (3) With the addition of surface currents and SSH, the model effectively captures the high Chl-a patches induced by eddies. This research presents a viable method for estimating surface Chl-a concentrations in regions where they are highly correlated with dynamic factors, using deep learning and comprehensive oceanic and atmospheric data.https://www.frontiersin.org/articles/10.3389/fmars.2025.1528921/fullResUnetchlorophyll-adeep learningSouth China Seaphysical factors |
| spellingShingle | Weiwei Fang Ao Li Ao Li Haoyu Jiang Haoyu Jiang Chan Shu Peng Xiu Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea Frontiers in Marine Science ResUnet chlorophyll-a deep learning South China Sea physical factors |
| title | Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea |
| title_full | Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea |
| title_fullStr | Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea |
| title_full_unstemmed | Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea |
| title_short | Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea |
| title_sort | leveraging resunet oceanic and atmospheric data for accurate chlorophyll a estimations in the south china sea |
| topic | ResUnet chlorophyll-a deep learning South China Sea physical factors |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1528921/full |
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