A Machine Learning Approach to Produce a Continuous Solar‐Induced Chlorophyll Fluorescence Over the Arctic Ocean

AbstractPhytoplankton 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 l