Vertically Resolved Global Ocean Light Models Using Machine Learning
The vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/24/5663 |
| _version_ | 1797379497911123968 |
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| author | Pannimpullath Remanan Renosh Jie Zhang Raphaëlle Sauzède Hervé Claustre |
| author_facet | Pannimpullath Remanan Renosh Jie Zhang Raphaëlle Sauzède Hervé Claustre |
| author_sort | Pannimpullath Remanan Renosh |
| collection | DOAJ |
| description | The vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths and photosynthetically available radiation (PAR) on a global scale. These models rely on the SOCA (Satellite Ocean Color merged with Argo data to infer bio-optical properties to depth) methodology, which is based on an artificial neural network (ANN). The new light models are trained with light profiles (ED/PAR) acquired from BioGeoChemical-Argo (BGC-Argo) floats. The model inputs consist of surface ocean color radiometry data (i.e., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></semantics></math></inline-formula>, PAR, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>k</mi><mi>d</mi></msub><mrow><mo>(</mo><mn>490</mn><mo>)</mo></mrow></mrow></semantics></math></inline-formula>) derived by satellite and extracted from the GlobColour database, temperature and salinity profiles originating from BGC-Argo, as well as temporal components (day of the year and local time in cyclic transformation). The model outputs correspond to ED profiles at the three wavelengths of the BGC-Argo measurements (i.e., 380, 412, and 490 nm) and PAR profiles. We assessed the retrieval of light profiles by these light models using three different datasets: BGC-Argo profiles that were not used for the training (i.e., 20% of the initial database); data from four independent BGC-Argo floats that were used neither for the training nor for the 20% validation dataset; and the SeaBASS database (in situ data collected from various oceanic cruises). The light models show satisfactory predictions when thus compared with real measurements. From the 20% validation database, the light models retrieve light variables with high accuracies (root mean squared error (RMSE)) of 76.42 μmol quanta m<sup>−2</sup> s<sup>−1</sup> for PAR and 0.04, 0.08, and 0.09 W m<sup>−2</sup> nm<sup>−1</sup> for ED380, ED412, and ED490, respectively. This corresponds to a median absolute percent error (MAPE) that ranges from 37% for ED490 and PAR to 39% for ED380 and ED412. The estimated accuracy metrics across these three validation datasets are consistent and demonstrate the robustness and suitability of these light models for diverse global ocean applications. |
| first_indexed | 2024-03-08T20:24:08Z |
| format | Article |
| id | doaj.art-705841a58f474c558bb8ba2404296745 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T20:24:08Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-705841a58f474c558bb8ba24042967452023-12-22T14:38:55ZengMDPI AGRemote Sensing2072-42922023-12-011524566310.3390/rs15245663Vertically Resolved Global Ocean Light Models Using Machine LearningPannimpullath Remanan Renosh0Jie Zhang1Raphaëlle Sauzède2Hervé Claustre3Laboratoire d’Océanographie de Villefranche, Institut de la Mer de Villefranche, Sorbonne Université, CNRS INSU, 06230 Villefranche-sur-Mer, FranceLaboratoire d’Océanographie de Villefranche, Institut de la Mer de Villefranche, Sorbonne Université, CNRS INSU, 06230 Villefranche-sur-Mer, FranceLaboratoire d’Océanographie de Villefranche, Institut de la Mer de Villefranche, Sorbonne Université, CNRS INSU, 06230 Villefranche-sur-Mer, FranceLaboratoire d’Océanographie de Villefranche, Institut de la Mer de Villefranche, Sorbonne Université, CNRS INSU, 06230 Villefranche-sur-Mer, FranceThe vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths and photosynthetically available radiation (PAR) on a global scale. These models rely on the SOCA (Satellite Ocean Color merged with Argo data to infer bio-optical properties to depth) methodology, which is based on an artificial neural network (ANN). The new light models are trained with light profiles (ED/PAR) acquired from BioGeoChemical-Argo (BGC-Argo) floats. The model inputs consist of surface ocean color radiometry data (i.e., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></semantics></math></inline-formula>, PAR, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>k</mi><mi>d</mi></msub><mrow><mo>(</mo><mn>490</mn><mo>)</mo></mrow></mrow></semantics></math></inline-formula>) derived by satellite and extracted from the GlobColour database, temperature and salinity profiles originating from BGC-Argo, as well as temporal components (day of the year and local time in cyclic transformation). The model outputs correspond to ED profiles at the three wavelengths of the BGC-Argo measurements (i.e., 380, 412, and 490 nm) and PAR profiles. We assessed the retrieval of light profiles by these light models using three different datasets: BGC-Argo profiles that were not used for the training (i.e., 20% of the initial database); data from four independent BGC-Argo floats that were used neither for the training nor for the 20% validation dataset; and the SeaBASS database (in situ data collected from various oceanic cruises). The light models show satisfactory predictions when thus compared with real measurements. From the 20% validation database, the light models retrieve light variables with high accuracies (root mean squared error (RMSE)) of 76.42 μmol quanta m<sup>−2</sup> s<sup>−1</sup> for PAR and 0.04, 0.08, and 0.09 W m<sup>−2</sup> nm<sup>−1</sup> for ED380, ED412, and ED490, respectively. This corresponds to a median absolute percent error (MAPE) that ranges from 37% for ED490 and PAR to 39% for ED380 and ED412. The estimated accuracy metrics across these three validation datasets are consistent and demonstrate the robustness and suitability of these light models for diverse global ocean applications.https://www.mdpi.com/2072-4292/15/24/5663BGC-ArgoED380ED412ED490global oceanlight models |
| spellingShingle | Pannimpullath Remanan Renosh Jie Zhang Raphaëlle Sauzède Hervé Claustre Vertically Resolved Global Ocean Light Models Using Machine Learning Remote Sensing BGC-Argo ED380 ED412 ED490 global ocean light models |
| title | Vertically Resolved Global Ocean Light Models Using Machine Learning |
| title_full | Vertically Resolved Global Ocean Light Models Using Machine Learning |
| title_fullStr | Vertically Resolved Global Ocean Light Models Using Machine Learning |
| title_full_unstemmed | Vertically Resolved Global Ocean Light Models Using Machine Learning |
| title_short | Vertically Resolved Global Ocean Light Models Using Machine Learning |
| title_sort | vertically resolved global ocean light models using machine learning |
| topic | BGC-Argo ED380 ED412 ED490 global ocean light models |
| url | https://www.mdpi.com/2072-4292/15/24/5663 |
| work_keys_str_mv | AT pannimpullathremananrenosh verticallyresolvedglobaloceanlightmodelsusingmachinelearning AT jiezhang verticallyresolvedglobaloceanlightmodelsusingmachinelearning AT raphaellesauzede verticallyresolvedglobaloceanlightmodelsusingmachinelearning AT herveclaustre verticallyresolvedglobaloceanlightmodelsusingmachinelearning |