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...
Published in: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Online Access: | https://doi.org/10.5194/isprs-annals-V-2-2020-949-2020 https://doaj.org/article/7fbf854757b04e86a31f27638c455dfe |
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ftdoajarticles:oai:doaj.org/article:7fbf854757b04e86a31f27638c455dfe 2023-05-15T17:35:32+02:00 ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING R. Sauzède J. E. Johnson H. Claustre G. Camps-Valls A. B. Ruescas 2020-08-01T00:00:00Z https://doi.org/10.5194/isprs-annals-V-2-2020-949-2020 https://doaj.org/article/7fbf854757b04e86a31f27638c455dfe EN eng Copernicus Publications https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/949/2020/isprs-annals-V-2-2020-949-2020.pdf https://doaj.org/toc/2194-9042 https://doaj.org/toc/2194-9050 doi:10.5194/isprs-annals-V-2-2020-949-2020 2194-9042 2194-9050 https://doaj.org/article/7fbf854757b04e86a31f27638c455dfe ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 949-956 (2020) Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 article 2020 ftdoajarticles https://doi.org/10.5194/isprs-annals-V-2-2020-949-2020 2022-12-31T03:44:14Z 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, b bp ) 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. b bp , 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 b bp , 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 b bp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 949 956 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
spellingShingle |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 R. Sauzède J. E. Johnson H. Claustre G. Camps-Valls A. B. Ruescas ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING |
topic_facet |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
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, b bp ) 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. b bp , 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 b bp , 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 b bp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale. |
format |
Article in Journal/Newspaper |
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 |
title |
ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING |
title_short |
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_sort |
estimation of oceanic particulate organic carbon with machine learning |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/isprs-annals-V-2-2020-949-2020 https://doaj.org/article/7fbf854757b04e86a31f27638c455dfe |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 949-956 (2020) |
op_relation |
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/949/2020/isprs-annals-V-2-2020-949-2020.pdf https://doaj.org/toc/2194-9042 https://doaj.org/toc/2194-9050 doi:10.5194/isprs-annals-V-2-2020-949-2020 2194-9042 2194-9050 https://doaj.org/article/7fbf854757b04e86a31f27638c455dfe |
op_doi |
https://doi.org/10.5194/isprs-annals-V-2-2020-949-2020 |
container_title |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
V-2-2020 |
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949 |
op_container_end_page |
956 |
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