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 mode..., , # Data from: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations

Dataset DOI: [10.5061/dryad.w6m905r1j](10.5061/dryad.w6m905r1j)

## Description of the data and file structure

Data are a subset of the database curated by Bouman et al. (2018; Photosynthesis-­irradiance parameters of phytoplankton: synthesis of a global data set. Earth System Science Data **10**: 251–266) and updated by Kulk et al. (2020; Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sensing **12**: 1–26). All measurements analyzed here were taken using 14C uptake as a measure of photosynthetic rate using incubations between 1.5-4 hours in duration. The database reports parameters for fitted PI relationships. We chose subsets of PI relationships originating from established regional sampling programs wherein the same locations are sampled across time and over a range of environmental conditions using consistent sa...,