High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning
This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution...
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ftunivmalta:oai:www.um.edu.mt:123456789/111254 2023-07-23T04:20:33+02:00 High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning Galdies, Charles Guerra, Roberta 2023 https://www.um.edu.mt/library/oar/handle/123456789/111254 https://doi.org/10.3390/w15081454 en eng MDPI AG Galdies, C. & Guerra, R. (2023). High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning. Water, 15(8), 1454. https://www.um.edu.mt/library/oar/handle/123456789/111254 doi:10.3390/w15081454 info:eu-repo/semantics/openAccess The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. Ocean acidification Hydrogen-ion concentration Deep learning (Machine learning) Earth Observation Remote sensing -- Equipment and supplies article 2023 ftunivmalta https://doi.org/10.3390/w15081454 2023-07-05T17:28:53Z This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution in the North Atlantic region (Long. −61.00° to −50.04° W; Lat. 24.99° to 34.96° N). The in situ reference dataset that was used for this study provided discrete underway measurements of DIC, TA, and pH collected by M/V Equinox in the North Atlantic Ocean. A unique list of co-temporal and co-located global daily environmental drivers derived from independent sources (using satellite remote sensing, model reanalyses, empirical algorithms, and depth soundings) were collected for this study at the highest possible spatial resolution (0.04° × 0.04°). The resulting ANN-estimated DIC, TA, and pH obtained by deep learning shows a high correspondence when verified against observations. This study demonstrates how a select number of geophysical information derived from EO and model reanalysis data can be used to estimate and understand the spatiotemporal variability of the oceanic carbonate system at a high spatiotemporal resolution. Further methodological improvements are being suggested. peer-reviewed Article in Journal/Newspaper North Atlantic Ocean acidification University of Malta: OAR@UM Water 15 8 1454 |
institution |
Open Polar |
collection |
University of Malta: OAR@UM |
op_collection_id |
ftunivmalta |
language |
English |
topic |
Ocean acidification Hydrogen-ion concentration Deep learning (Machine learning) Earth Observation Remote sensing -- Equipment and supplies |
spellingShingle |
Ocean acidification Hydrogen-ion concentration Deep learning (Machine learning) Earth Observation Remote sensing -- Equipment and supplies Galdies, Charles Guerra, Roberta High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning |
topic_facet |
Ocean acidification Hydrogen-ion concentration Deep learning (Machine learning) Earth Observation Remote sensing -- Equipment and supplies |
description |
This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution in the North Atlantic region (Long. −61.00° to −50.04° W; Lat. 24.99° to 34.96° N). The in situ reference dataset that was used for this study provided discrete underway measurements of DIC, TA, and pH collected by M/V Equinox in the North Atlantic Ocean. A unique list of co-temporal and co-located global daily environmental drivers derived from independent sources (using satellite remote sensing, model reanalyses, empirical algorithms, and depth soundings) were collected for this study at the highest possible spatial resolution (0.04° × 0.04°). The resulting ANN-estimated DIC, TA, and pH obtained by deep learning shows a high correspondence when verified against observations. This study demonstrates how a select number of geophysical information derived from EO and model reanalysis data can be used to estimate and understand the spatiotemporal variability of the oceanic carbonate system at a high spatiotemporal resolution. Further methodological improvements are being suggested. peer-reviewed |
format |
Article in Journal/Newspaper |
author |
Galdies, Charles Guerra, Roberta |
author_facet |
Galdies, Charles Guerra, Roberta |
author_sort |
Galdies, Charles |
title |
High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning |
title_short |
High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning |
title_full |
High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning |
title_fullStr |
High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning |
title_full_unstemmed |
High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning |
title_sort |
high resolution estimation of ocean dissolved inorganic carbon, total alkalinity and ph based on deep learning |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://www.um.edu.mt/library/oar/handle/123456789/111254 https://doi.org/10.3390/w15081454 |
genre |
North Atlantic Ocean acidification |
genre_facet |
North Atlantic Ocean acidification |
op_relation |
Galdies, C. & Guerra, R. (2023). High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning. Water, 15(8), 1454. https://www.um.edu.mt/library/oar/handle/123456789/111254 doi:10.3390/w15081454 |
op_rights |
info:eu-repo/semantics/openAccess The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
op_doi |
https://doi.org/10.3390/w15081454 |
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Water |
container_volume |
15 |
container_issue |
8 |
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1454 |
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1772185026384887808 |