Process-Oriented Estimation of Chlorophyll-a Vertical Profile in the Mediterranean Sea Using MODIS and Oceanographic Float Products

Abstract

Reconstructing chlorophyll-a (Chl-a) vertical profile is a promising approach for investigating the internal structure of marine ecosystem. Given that the process of profile classification in current process-oriented profile inversion methods are either too subjective or too complex, a novel Chl-a profile reconstruction method was proposed incorporating both a novel binary tree profile classification model and a profile inversion model in the Mediterranean Sea. The binary tree profile classification model was established based on a priori knowledge provided by clustering Chl-a profiles measured by BGC-Argo floats performed by the profile classification model (PCM), an advanced unsupervised machine learning clustering method. The profile inversion model contains the relationships between the shape-dependent parameters of the nonuniform Chl-a profile and the corresponding Chl-a surface concentration derived from satellite observations. According to quantitative evaluation, the proposed profile classification model reached an overall accuracy of 89%, and the mean absolute percent deviation (MAPD) of the proposed profile inversion model ranged from 12%–37% under different shape-dependent parameters. By generating monthly three dimensions Chl-a concentration from 2011 to 2018, the proposed process-oriented method exhibits great application potential in investigating the spatial and temporal characteristics of Chl-a profiles and even the water column total biomass throughout the Mediterranean Sea.