This study analyzes remotely sensed chlorophyll a (Chl a) concentration as an indicator of ocean productivity in the European Arctic Corridor (the Norwegian, Greenland, and Barents Seas), and its connection to physical environmental parameters: Photosynthetically Active Radiation (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS). Using the Random Forest Machine Learning algorithm in the Classifier modification we created models describing a correlation between Chl a and environmental parameters, and retrieved a total area of high-productivity zones (Chl a is more than 1 mg m−3) based on these correlations.
This research was funded by the Saint Petersburg State University, project N 94033410.