ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING
Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-08-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/949/2020/isprs-annals-V-2-2020-949-2020.pdf |
| _version_ | 1818297199376728064 |
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| author | R. Sauzède J. E. Johnson H. Claustre G. Camps-Valls A. B. Ruescas |
| author_facet | R. Sauzède J. E. Johnson H. Claustre G. Camps-Valls A. B. Ruescas |
| author_sort | R. Sauzède |
| collection | DOAJ |
| description | Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, <i>b<sub>bp</sub></i>) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. <i>b<sub>bp</sub></i>, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of <i>b<sub>bp</sub></i>, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the <i>b<sub>bp</sub></i> profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale. |
| first_indexed | 2024-12-13T04:15:38Z |
| format | Article |
| id | doaj.art-7fbf854757b04e86a31f27638c455dfe |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-13T04:15:38Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-7fbf854757b04e86a31f27638c455dfe2022-12-21T23:59:54ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502020-08-01V-2-202094995610.5194/isprs-annals-V-2-2020-949-2020ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNINGR. Sauzède0J. E. Johnson1H. Claustre2G. Camps-Valls3A. B. Ruescas4CNRS-INSU, Sorbonne Université, Institut de la Mer de Villefranche, Villefranche-Sur-Mer, FranceUniversity of Valencia, Image Processing Laboratory, 46980 Paterna (València), SpainCNRS-INSU, Sorbonne Université, Institut de la Mer de Villefranche, Villefranche-Sur-Mer, FranceUniversity of Valencia, Image Processing Laboratory, 46980 Paterna (València), SpainUniversity of Valencia, Image Processing Laboratory, 46980 Paterna (València), SpainUnderstanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, <i>b<sub>bp</sub></i>) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. <i>b<sub>bp</sub></i>, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of <i>b<sub>bp</sub></i>, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the <i>b<sub>bp</sub></i> profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/949/2020/isprs-annals-V-2-2020-949-2020.pdf |
| spellingShingle | R. Sauzède J. E. Johnson H. Claustre G. Camps-Valls A. B. Ruescas ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING |
| title_full | ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING |
| title_fullStr | ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING |
| title_full_unstemmed | ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING |
| title_short | ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING |
| title_sort | estimation of oceanic particulate organic carbon with machine learning |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/949/2020/isprs-annals-V-2-2020-949-2020.pdf |
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