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A depth-resolved artificial neural network model of marine phytoplankton primary production

Mattei, F., Franceschini, S., Scardi, M.
Ecological modelling 2018 v.382 pp. 51-62
aquatic food webs, carbon cycle, energy flow, neural networks, oceanography, phytoplankton, primary productivity, remote sensing, spatial distribution
Marine phytoplankton primary production is an extremely important process and its estimates play a major role not only in biological oceanography, but also in a broader context, due to its relationship with oceanic food webs, energy fluxes, carbon cycle and Earth’s climate.The measurement of this process is both expensive and time consuming. Therefore, indirect methods, which can estimate phytoplankton primary production using only remotely sensed predictive information, have many advantages. We describe the development of a depth-resolved model based on an Artificial Neural Network for estimating global phytoplankton primary production. Furthermore, we applied two different approaches, based on input perturbation analysis and on connection weights, to assess the relative importance of the predictive variables. Finally, we compared the results of our depth-resolved model with a previous depth-integrated solution, showing that through the depth-resolution we gained not only useful information on the vertical distribution of the estimated primary production, but also an enhanced accuracy in its depth-integrated estimates.