Neural network model output of marine phytoplankton primary production (Global Ocean)
Global phytoplankton production monthly maps for 2017 are produced using an artificial neural network to perform a generalized nonlinear regression of PP on several predictive variables, including latitude, longitude, day length, MLD, SST, PBopt computed according to Behrenfeld and Falkowski (1997), PAR and CHL(0 m). More details about this model can be found in Scardi (2001).
Behrenfeld, M. J., Falkowski, P. G. (1997), Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnology & Oceanography, 42(1), 1–20. Scardi, M. (2001), Advances in neural network modeling of phytoplankton primary production, Ecological Modelling, 146, 33–45.
Data and resources
Additional information
| Temporal extent | 2017-01-01 – 2017-12-31 |
|---|---|
| Date (creation) | 2018-01-29 |
| Date (revision) | 2018-02-12 |
| Date (publication) | 2018-05-09 |
| Date (publication) | 2018-10-10 |
| Status | onGoing |
| Update frequency | asNeeded |
| Metadata language | en |
| License | Conditions for access and use apply |
| Source metadata | ISO 19115 XML |
| Last updated | 2026-11-04 |
| Created | 2026-11-04 |
| API | package_show |