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\begin{document}

\title{A machine learning approach to produce a continuous solar-induced
chlorophyll fluorescence dataset for understanding Ocean productivity}



\author[1]{Nima Madani}%
\author[1]{Nicholas C Parazoo}%
\author[2]{Manfredi Manizza}%
\author[3]{Abhishek Chatterjee}%
\author[4]{Dustin Carroll}%
\author[5]{Dimitris Menemenlis}%
\author[6]{Vincent le Fouest}%
\author[7]{Atsushi Matsuoka}%
\author[5]{Kelly Luis}%
\author[8]{Camila Serra-Pompei}%
\author[1]{Charles E. Miller}%
\affil[1]{Jet Propulsion Laboratory}%
\affil[2]{Scripps Institution of Oceanography}%
\affil[3]{NASA Jet Propulsion Laboratory}%
\affil[4]{Moss Landing Marine Laboratories}%
\affil[5]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[6]{Université de La Rochelle / CNRS}%
\affil[7]{University of New Hampshire}%
\affil[8]{Massachusetts Institute of Technology}%


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  \date{June 18, 2024}


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\begin{abstract}
Phytoplankton primary production is a crucial component of Arctic Ocean
(AO) biogeochemistry, playing a pivotal role in the carbon cycling by
supporting higher trophic levels and removing atmospheric carbon
dioxide. The advent of satellite observations measuring chlorophyll a
concentration (Chl\_ a) has yielded unprecedented insights into the
distribution of AO phytoplankton, enhancing our ability to assess
oceanic productivity. However, the optical properties of AO waters
differ significantly from those of lower-latitude waters, and standard
Chl\_a algorithms perform poorly in the AO. In particular, Chl\_a
retrievals are challenged by interferences from other marine
constituents including higher pigment packaging and higher proportion of
light absorption by colored dissolved organic matter. To derive
phytoplankton-originating signature as well as mitigate those effects,
solar-induced chlorophyll fluorescence (SIF) emerges as a valuable tool
for acquiring physiological insights into the direct photosynthetic
processes in the AO. In this study, we leverage satellite-based SIF
measurements to assess their correlation with a set of predictive
factors influencing phytoplankton photosynthesis. We extend the temporal
coverage of AO SIF data to cover the period 2004 - 2020. This novel
dataset offers a pathway to monitor the physiological interactions of
phytoplankton with changes in climate, promising to significantly
improve our understanding of the Arctic water's productivity. The
application of this data is expected to provide insights into how
phytoplankton respond to shifts in environmental changes, contributing
to a more nuanced understanding of their role in High-Latitude Northern
Oceans ecosystems.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Arctic Ocean chlorophyll_c.docx`  available at \url{https://authorea.com/users/563938/articles/734362-a-machine-learning-approach-to-produce-a-continuous-solar-induced-chlorophyll-fluorescence-dataset-for-understanding-ocean-productivity}

\textbf{Hosted file}

\verb`Supplimantary_b.docx`  available at \url{https://authorea.com/users/563938/articles/734362-a-machine-learning-approach-to-produce-a-continuous-solar-induced-chlorophyll-fluorescence-dataset-for-understanding-ocean-productivity}

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