Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost
Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), disso...
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ftmdpi:oai:mdpi.com:/2072-4292/13/14/2805/ 2023-08-20T04:08:11+02:00 Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost Hongwei Sun Junyu He Yihui Chen Boyu Zhao agris 2021-07-16 application/pdf https://doi.org/10.3390/rs13142805 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs13142805 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 14; Pages: 2805 sea surface pCO 2 ocean color remote sensing CatBoost algorithm temporal and spatial distribution Text 2021 ftmdpi https://doi.org/10.3390/rs13142805 2023-08-01T02:12:35Z Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions. Text North Atlantic Ocean acidification MDPI Open Access Publishing Remote Sensing 13 14 2805 |
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MDPI Open Access Publishing |
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English |
topic |
sea surface pCO 2 ocean color remote sensing CatBoost algorithm temporal and spatial distribution |
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sea surface pCO 2 ocean color remote sensing CatBoost algorithm temporal and spatial distribution Hongwei Sun Junyu He Yihui Chen Boyu Zhao Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost |
topic_facet |
sea surface pCO 2 ocean color remote sensing CatBoost algorithm temporal and spatial distribution |
description |
Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions. |
format |
Text |
author |
Hongwei Sun Junyu He Yihui Chen Boyu Zhao |
author_facet |
Hongwei Sun Junyu He Yihui Chen Boyu Zhao |
author_sort |
Hongwei Sun |
title |
Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost |
title_short |
Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost |
title_full |
Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost |
title_fullStr |
Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost |
title_full_unstemmed |
Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost |
title_sort |
space-time sea surface pco2 estimation in the north atlantic based on catboost |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13142805 |
op_coverage |
agris |
genre |
North Atlantic Ocean acidification |
genre_facet |
North Atlantic Ocean acidification |
op_source |
Remote Sensing; Volume 13; Issue 14; Pages: 2805 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs13142805 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13142805 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
14 |
container_start_page |
2805 |
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1774720337603198976 |