A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean
Abstract Phytoplankton primary production is a crucial component of Arctic Ocean (AO) biogeochemistry, playing a pivotal role in carbon cycling by supporting higher trophic levels and removing atmospheric carbon dioxide. The advent of satellite observations measuring chlorophyll a concentration (Chl...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Online Access: | https://doi.org/10.1029/2024JH000215 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850066425685737472 |
|---|---|
| author | Nima Madani Nicholas C. Parazoo Manfredi Manizza Abhishek Chatterjee Dustin Carroll Dimitris Menemenlis Vincent Le Fouest Atsushi Matsuoka Kelly M. Luis Camila Serra‐Pompei Charles E. Miller |
| author_facet | Nima Madani Nicholas C. Parazoo Manfredi Manizza Abhishek Chatterjee Dustin Carroll Dimitris Menemenlis Vincent Le Fouest Atsushi Matsuoka Kelly M. Luis Camila Serra‐Pompei Charles E. Miller |
| author_sort | Nima Madani |
| collection | DOAJ |
| description | Abstract Phytoplankton primary production is a crucial component of Arctic Ocean (AO) biogeochemistry, playing a pivotal role in carbon cycling by supporting higher trophic levels and removing atmospheric carbon dioxide. The advent of satellite observations measuring chlorophyll a concentration (Chl_a) has provided unprecedented insights into the distribution of AO phytoplankton, enhancing our ability to assess oceanic net primary production (NPP). However, the optical properties of AO waters differ significantly from those of the lower‐latitude waters, complicating remotely sensed Chl_a retrievals. To mitigate these deficiencies, solar‐induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for gaining physiological insights into the direct photosynthetic processes of the AO. However, the temporal coverage of satellite SIF data makes long‐term analysis of Chl_a photosynthetic activity challenging. In this study, we leverage satellite‐based SIF measurements from 2018 to 2021 to assess their correlation with a set of predictive factors influencing phytoplankton photosynthesis. Generally, observed SIF over the AO showed a higher correlation with normalized fluorescence line height (NFLH) compared to Chl_a. We extended the temporal coverage of the original SIF data to encompass the period from 2004 to 2020. The extended record revealed noticeable differences between SIF, and satellite‐based Chl_a, and NFLH observations. Our novel data set offers a pathway forward to monitor the physiological interactions of phytoplankton with climate changes, promising to significantly improve our understanding of Arctic waters productivity. The application of this data is expected to provide new insights into how phytoplankton respond to environmental shifts, contributing to a more nuanced understanding of their role in high‐latitude marine ecosystems. |
| format | Article |
| id | doaj-art-ba255c05dc5d4603b9fd18d64cbd9d40 |
| institution | DOAJ |
| issn | 2993-5210 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-ba255c05dc5d4603b9fd18d64cbd9d402025-08-20T02:48:45ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102024-12-0114n/an/a10.1029/2024JH000215A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic OceanNima Madani0Nicholas C. Parazoo1Manfredi Manizza2Abhishek Chatterjee3Dustin Carroll4Dimitris Menemenlis5Vincent Le Fouest6Atsushi Matsuoka7Kelly M. Luis8Camila Serra‐Pompei9Charles E. Miller10UCLA Joint Institute for Regional Earth System Science and Engineering Los Angeles CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAGeosciences Research Division Scripps Institution of Oceanography University of California San Diego La Jolla CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USALittoral Environnement et Sociétés UMR 7266 Université de La Rochelle La Rochelle FranceInstitute for the Study of Earth, Oceans, and Space University of New Hampshire Durham NH USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USACenter for Climate Change Science Massachusetts Institute of Technology Cambridge MA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAAbstract Phytoplankton primary production is a crucial component of Arctic Ocean (AO) biogeochemistry, playing a pivotal role in carbon cycling by supporting higher trophic levels and removing atmospheric carbon dioxide. The advent of satellite observations measuring chlorophyll a concentration (Chl_a) has provided unprecedented insights into the distribution of AO phytoplankton, enhancing our ability to assess oceanic net primary production (NPP). However, the optical properties of AO waters differ significantly from those of the lower‐latitude waters, complicating remotely sensed Chl_a retrievals. To mitigate these deficiencies, solar‐induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for gaining physiological insights into the direct photosynthetic processes of the AO. However, the temporal coverage of satellite SIF data makes long‐term analysis of Chl_a photosynthetic activity challenging. In this study, we leverage satellite‐based SIF measurements from 2018 to 2021 to assess their correlation with a set of predictive factors influencing phytoplankton photosynthesis. Generally, observed SIF over the AO showed a higher correlation with normalized fluorescence line height (NFLH) compared to Chl_a. We extended the temporal coverage of the original SIF data to encompass the period from 2004 to 2020. The extended record revealed noticeable differences between SIF, and satellite‐based Chl_a, and NFLH observations. Our novel data set offers a pathway forward to monitor the physiological interactions of phytoplankton with climate changes, promising to significantly improve our understanding of Arctic waters productivity. The application of this data is expected to provide new insights into how phytoplankton respond to environmental shifts, contributing to a more nuanced understanding of their role in high‐latitude marine ecosystems.https://doi.org/10.1029/2024JH000215 |
| spellingShingle | Nima Madani Nicholas C. Parazoo Manfredi Manizza Abhishek Chatterjee Dustin Carroll Dimitris Menemenlis Vincent Le Fouest Atsushi Matsuoka Kelly M. Luis Camila Serra‐Pompei Charles E. Miller A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean Journal of Geophysical Research: Machine Learning and Computation |
| title | A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean |
| title_full | A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean |
| title_fullStr | A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean |
| title_full_unstemmed | A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean |
| title_short | A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean |
| title_sort | machine learning approach to produce a continuous solar induced chlorophyll fluorescence over the arctic ocean |
| url | https://doi.org/10.1029/2024JH000215 |
| work_keys_str_mv | AT nimamadani amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT nicholascparazoo amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT manfredimanizza amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT abhishekchatterjee amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT dustincarroll amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT dimitrismenemenlis amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT vincentlefouest amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT atsushimatsuoka amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT kellymluis amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT camilaserrapompei amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT charlesemiller amachinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT nimamadani machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT nicholascparazoo machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT manfredimanizza machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT abhishekchatterjee machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT dustincarroll machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT dimitrismenemenlis machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT vincentlefouest machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT atsushimatsuoka machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT kellymluis machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT camilaserrapompei machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean AT charlesemiller machinelearningapproachtoproduceacontinuoussolarinducedchlorophyllfluorescenceoverthearcticocean |