Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data
In this paper, the central Indian Ocean (60°–95°E, 0°–37°S) has been selected as the research area, and Argo salinity data are used as the measured values. The Catboost algorithm is introduced for the first time to retrieve sea surface salinity, and a comparison is made with the traditional artifici...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2023.2298575 |
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| author | Ziyi Gong Hongchang He Donglin Fan You Zeng Zhenhao Liu Bozhi Pan |
| author_facet | Ziyi Gong Hongchang He Donglin Fan You Zeng Zhenhao Liu Bozhi Pan |
| author_sort | Ziyi Gong |
| collection | DOAJ |
| description | In this paper, the central Indian Ocean (60°–95°E, 0°–37°S) has been selected as the research area, and Argo salinity data are used as the measured values. The Catboost algorithm is introduced for the first time to retrieve sea surface salinity, and a comparison is made with the traditional artificial neural network (ANN) and random forest (RF) machine learning algorithm. The results show that: (1) Through linear fitting with the Argo salinity, the R2 of the three machine learning methods are 0.9299, 0.88 and 0.83, respectively. The corresponding RMSE were 0.2360, 0.3004, and 0.3156 psu, and MAE were 0.1816, 0.2486, and 0.2641 psu, respectively. (2) The spatial distribution of salinity of Argo and SMOS was compared with the inversion results of the model. It was found that the salinity of the sea area was lower at (83°–88°E, 24°–27°S) and (68°–72°E, 17°–20°S), and higher at 30°–35° south latitude, showing consistent with Argo. (3) The stability of the model was independently verified using the data from January to March 2020, and it was found that the R2 of the RF model shows the best stability, while the R2 of the ANN model shows the worst stability. |
| format | Article |
| id | doaj-art-9feab1cda3c5493d81f8e1deb22847cb |
| institution | Kabale University |
| issn | 1712-7971 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj-art-9feab1cda3c5493d81f8e1deb22847cb2025-01-02T11:34:20ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712024-12-0150110.1080/07038992.2023.22985752298575Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite DataZiyi Gong0Hongchang He1Donglin Fan2You Zeng3Zhenhao Liu4Bozhi Pan5College of Geomatics and Geoinformation, Guilin University of TechnologyCollege of Geomatics and Geoinformation, Guilin University of TechnologyCollege of Geomatics and Geoinformation, Guilin University of TechnologyCollege of Geomatics and Geoinformation, Guilin University of TechnologyCollege of Geomatics and Geoinformation, Guilin University of TechnologyCollege of Geomatics and Geoinformation, Guilin University of TechnologyIn this paper, the central Indian Ocean (60°–95°E, 0°–37°S) has been selected as the research area, and Argo salinity data are used as the measured values. The Catboost algorithm is introduced for the first time to retrieve sea surface salinity, and a comparison is made with the traditional artificial neural network (ANN) and random forest (RF) machine learning algorithm. The results show that: (1) Through linear fitting with the Argo salinity, the R2 of the three machine learning methods are 0.9299, 0.88 and 0.83, respectively. The corresponding RMSE were 0.2360, 0.3004, and 0.3156 psu, and MAE were 0.1816, 0.2486, and 0.2641 psu, respectively. (2) The spatial distribution of salinity of Argo and SMOS was compared with the inversion results of the model. It was found that the salinity of the sea area was lower at (83°–88°E, 24°–27°S) and (68°–72°E, 17°–20°S), and higher at 30°–35° south latitude, showing consistent with Argo. (3) The stability of the model was independently verified using the data from January to March 2020, and it was found that the R2 of the RF model shows the best stability, while the R2 of the ANN model shows the worst stability.http://dx.doi.org/10.1080/07038992.2023.2298575 |
| spellingShingle | Ziyi Gong Hongchang He Donglin Fan You Zeng Zhenhao Liu Bozhi Pan Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data Canadian Journal of Remote Sensing |
| title | Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data |
| title_full | Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data |
| title_fullStr | Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data |
| title_full_unstemmed | Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data |
| title_short | Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data |
| title_sort | comparison of machine learning inversion methods for salinity in the central indian ocean based on smos satellite data |
| url | http://dx.doi.org/10.1080/07038992.2023.2298575 |
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