Data from: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations

Photosynthesis-irradiance (PI) relationships are important for
phytoplankton ecology and for 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 database of in-situ PI relationships to build models
that predict the seasonal cycle of PI parameters as a function of
satellite-observed variables. Using only surface light, temperature, and
chlorophyll, we achieve an R2 of 58% for predicting photosynthesis rates
at saturating light and an R2 of 78% for predicting the light
saturation parameter. Predictability is maximized when averaging
environmental covariates over 30-day and
25-day timescales, respectively, indicating that environmental
history and community turnover timescales are important for predicting
in-situ PI relationships. These results will help improve the
parameterization of satellite-based primary production models and quantify
emergent environmental integration timescales in photosynthetic
communities.