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Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations
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  • Gregory L Britten,
  • Bror Jönsson,
  • Gemma Kulk,
  • Heather A Bouman,
  • Michael J Follows,
  • Shubha Sathyendranath
Gregory L Britten
Biology Department, Woods Hole Oceanographic Institution, Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology

Corresponding Author:[email protected]

Author Profile
Bror Jönsson
Earth Observation Science and Applications, Plymouth Marine Laboratory
Gemma Kulk
Earth Observation Science and Applications, Plymouth Marine Laboratory, National Centre for Earth Observation, Plymouth Marine Laboratory
Heather A Bouman
Department of Earth Sciences, University of Oxford
Michael J Follows
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology
Shubha Sathyendranath
Earth Observation Science and Applications, Plymouth Marine Laboratory, National Centre for Earth Observation, Plymouth Marine Laboratory

Abstract

Photosynthesis-irradiance (PI) relationships are important for phytoplankton ecology and quantifying carbon fixation rates in the environment. However, the parameters of PI relationships are typically unknown across space and time. Here we use machine learning, satellite remote-sensing, and a global database of in-situ PI relationships to build models that predict PI parameters globally as a function of satellite-observed variables. Using only surface light, temperature, and chlorophyll, we achieve an R2 of 76% for predicting photosynthesis rates at saturating light (𝑃Bmax) and an R2 of 58% for predicting the light saturation parameter (𝐸k). Predictability is maximized when averaging covariates over monthly timescales, indicating that environmental history and community turnover are important for predicting PI relationships. These results will help improve satellite-based primary production models, quantify emergent timescales in photosynthetic communities, and provide global estimates of photosynthetic parameters that can be used in plankton ecology and biogeochemistry models.
06 Sep 2024Submitted to ESS Open Archive
09 Sep 2024Published in ESS Open Archive