The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK
Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrop...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2024.1426547/full |
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| author | Solomon White Tiago Silva Laurent O. Amoudry Evangelos Spyrakos Adrien Martin Adrien Martin Encarni Medina-Lopez |
| author_facet | Solomon White Tiago Silva Laurent O. Amoudry Evangelos Spyrakos Adrien Martin Adrien Martin Encarni Medina-Lopez |
| author_sort | Solomon White |
| collection | DOAJ |
| description | Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions. |
| format | Article |
| id | doaj-art-75e04d538c8a44d8bc894c0f8ccac96a |
| institution | OA Journals |
| issn | 2296-665X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Environmental Science |
| spelling | doaj-art-75e04d538c8a44d8bc894c0f8ccac96a2025-08-20T02:38:49ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2024-12-011210.3389/fenvs.2024.14265471426547The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UKSolomon White0Tiago Silva1Laurent O. Amoudry2Evangelos Spyrakos3Adrien Martin4Adrien Martin5Encarni Medina-Lopez6Institute for Infrastructure and Environment, School of Engineering, The University of Edinburgh, Edinburgh, United KingdomCentre for Environment, Fisheries and Aquaculture Science (CEFAS), Lowestoft, United KingdomNational Oceanography Centre, Liverpool, United KingdomDepartment of Biological and Environmental Sciences, University of Stirling, Stirling, United KingdomNational Oceanography Centre, Southampton, United KingdomNOVELTIS, Labège, FranceInstitute for Infrastructure and Environment, School of Engineering, The University of Edinburgh, Edinburgh, United KingdomUnderstanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions.https://www.frontiersin.org/articles/10.3389/fenvs.2024.1426547/fullmachine learningsatellite multispectral imagerycoastal oceanographyexplainable AIocean colourtemperature |
| spellingShingle | Solomon White Tiago Silva Laurent O. Amoudry Evangelos Spyrakos Adrien Martin Adrien Martin Encarni Medina-Lopez The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK Frontiers in Environmental Science machine learning satellite multispectral imagery coastal oceanography explainable AI ocean colour temperature |
| title | The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK |
| title_full | The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK |
| title_fullStr | The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK |
| title_full_unstemmed | The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK |
| title_short | The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK |
| title_sort | colours of the ocean using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas the gulf of mexico and the uk |
| topic | machine learning satellite multispectral imagery coastal oceanography explainable AI ocean colour temperature |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2024.1426547/full |
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