Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios
Coastal phytoplankton blooms pose significant environmental challenges, yet spatiotemporal analyses of bloom dynamics under ocean warming and eutrophication remain limited. To address this, we developed machine learning-based regression and classification models for predicting bloom areas and warnin...
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| Format: | Article |
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000306 |
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| author | Siqi Wang Shuzhe Huang Yinguo Qiu Xiang Zhang Chao Wang Nengcheng Chen |
| author_facet | Siqi Wang Shuzhe Huang Yinguo Qiu Xiang Zhang Chao Wang Nengcheng Chen |
| author_sort | Siqi Wang |
| collection | DOAJ |
| description | Coastal phytoplankton blooms pose significant environmental challenges, yet spatiotemporal analyses of bloom dynamics under ocean warming and eutrophication remain limited. To address this, we developed machine learning-based regression and classification models for predicting bloom areas and warning levels. These models incorporate remote sensing data and key environmental variables from Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs under different climate change scenarios. We evaluated multiple machine learning approaches including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Tree (CART), Extreme Gradient Boosting (XGboost), and Light Gradient Boosting Machine (LightGBM) for their predictive capabilities. The LightGBM model, incorporating multi-season remote sensing data and key variables, achieved the highest accuracy, with R-values of 0.95 for warning level classification and 0.6 for bloom area regression. The spatial autocorrelation analysis validated the robustness of our models, demonstrating minimal cross-correlation between training and testing datasets. Furthermore, pixel-level analysis identified the East China Sea as the most bloom-prone region, with consistently higher bloom frequency and magnitude, particularly during summer. Under the historical scenario (incorporating both anthropogenic and natural forcings), we observed higher bloom frequencies and broader area variations compared to scenarios with isolated forcings. Notably, there was a trend toward more frequent yet smaller-scale blooms, with an increase in minor bloom occurrences despite a decrease in extreme events. Critical factors influencing bloom dynamics included sea surface temperature, air temperature, wind speed, sea level pressure, salinity, and nutrient concentrations. Our findings highlight satellite data's importance in understanding anthropogenic-natural factor interactions on coastal blooms, emphasizing the need for targeted nutrient management in vulnerable areas. |
| format | Article |
| id | doaj-art-18d0348ebe7148e5901b51e86f48503a |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-18d0348ebe7148e5901b51e86f48503a2025-08-20T03:47:20ZengElsevierScience of Remote Sensing2666-01722025-06-011110022410.1016/j.srs.2025.100224Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenariosSiqi Wang0Shuzhe Huang1Yinguo Qiu2Xiang Zhang3Chao Wang4Nengcheng Chen5National Engineering Research Centre of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China; State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, ChinaNational Engineering Research Centre of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China; Hubei Luojia Laboratory, Wuhan University, Wuhan, 430079, China; Corresponding author. National Engineering Research Centre of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan, 430079, ChinaNational Engineering Research Centre of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China; Hubei Luojia Laboratory, Wuhan University, Wuhan, 430079, ChinaCoastal phytoplankton blooms pose significant environmental challenges, yet spatiotemporal analyses of bloom dynamics under ocean warming and eutrophication remain limited. To address this, we developed machine learning-based regression and classification models for predicting bloom areas and warning levels. These models incorporate remote sensing data and key environmental variables from Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs under different climate change scenarios. We evaluated multiple machine learning approaches including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Tree (CART), Extreme Gradient Boosting (XGboost), and Light Gradient Boosting Machine (LightGBM) for their predictive capabilities. The LightGBM model, incorporating multi-season remote sensing data and key variables, achieved the highest accuracy, with R-values of 0.95 for warning level classification and 0.6 for bloom area regression. The spatial autocorrelation analysis validated the robustness of our models, demonstrating minimal cross-correlation between training and testing datasets. Furthermore, pixel-level analysis identified the East China Sea as the most bloom-prone region, with consistently higher bloom frequency and magnitude, particularly during summer. Under the historical scenario (incorporating both anthropogenic and natural forcings), we observed higher bloom frequencies and broader area variations compared to scenarios with isolated forcings. Notably, there was a trend toward more frequent yet smaller-scale blooms, with an increase in minor bloom occurrences despite a decrease in extreme events. Critical factors influencing bloom dynamics included sea surface temperature, air temperature, wind speed, sea level pressure, salinity, and nutrient concentrations. Our findings highlight satellite data's importance in understanding anthropogenic-natural factor interactions on coastal blooms, emphasizing the need for targeted nutrient management in vulnerable areas.http://www.sciencedirect.com/science/article/pii/S2666017225000306Phytoplankton bloomsCoastal seaClimate changeEutrophicationMachine learningWater management |
| spellingShingle | Siqi Wang Shuzhe Huang Yinguo Qiu Xiang Zhang Chao Wang Nengcheng Chen Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios Science of Remote Sensing Phytoplankton blooms Coastal sea Climate change Eutrophication Machine learning Water management |
| title | Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios |
| title_full | Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios |
| title_fullStr | Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios |
| title_full_unstemmed | Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios |
| title_short | Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios |
| title_sort | remote sensing driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios |
| topic | Phytoplankton blooms Coastal sea Climate change Eutrophication Machine learning Water management |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000306 |
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