An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea
Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface d...
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/13/3207 |
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| author | Jifeng Qi Chuanyu Liu Jianwei Chi Delei Li Le Gao Baoshu Yin |
| author_facet | Jifeng Qi Chuanyu Liu Jianwei Chi Delei Li Le Gao Baoshu Yin |
| author_sort | Jifeng Qi |
| collection | DOAJ |
| description | Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations. |
| first_indexed | 2024-03-09T12:35:42Z |
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| id | doaj.art-3406660934254945a8054c0fdda2bd9c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:35:42Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3406660934254945a8054c0fdda2bd9c2023-11-30T22:23:49ZengMDPI AGRemote Sensing2072-42922022-07-011413320710.3390/rs14133207An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China SeaJifeng Qi0Chuanyu Liu1Jianwei Chi2Delei Li3Le Gao4Baoshu Yin5CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, ChinaCAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaReconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations.https://www.mdpi.com/2072-4292/14/13/3207AI oceanographymachine learningensemble modelingocean subsurface thermal structureSouth China Seasatellite oceanography |
| spellingShingle | Jifeng Qi Chuanyu Liu Jianwei Chi Delei Li Le Gao Baoshu Yin An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea Remote Sensing AI oceanography machine learning ensemble modeling ocean subsurface thermal structure South China Sea satellite oceanography |
| title | An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea |
| title_full | An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea |
| title_fullStr | An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea |
| title_full_unstemmed | An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea |
| title_short | An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea |
| title_sort | ensemble based machine learning model for estimation of subsurface thermal structure in the south china sea |
| topic | AI oceanography machine learning ensemble modeling ocean subsurface thermal structure South China Sea satellite oceanography |
| url | https://www.mdpi.com/2072-4292/14/13/3207 |
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