Gregory L. Britten, Bror Jönsson, Gemma Kulk, Heather A. Bouman, Michael J. Follows, Shubha Sathyendranath (2025), Predicting photosynthesis–irradiance relationships from satellite remote-sensing observations, Limnology and Oceanography, doi: 10.1002/lol2.70062

Description:

Britten et al. (2025) explores how satellite remote-sensing data can be used to predict photosynthesis–irradiance (P–E) relationships in marine ecosystems. These relationships describe how phytoplankton photosynthesis responds to varying light levels, which is critical for estimating primary production in the ocean. Traditionally, P–E curves are derived from in situ measurements, which are spatially and temporally limited. The authors propose a method that leverages ocean color data and machine learning models to infer P–E parameters globally. Their approach improves coverage and reduces uncertainty in carbon flux estimates, offering a scalable solution for monitoring ocean productivity under changing climate conditions.