A Machine Learning Approach to Chlorophyll a Time Series Analysis in the Mediterranean Sea

Understanding the dynamics of natural system is a crucial task in ecology especially when climate change is taken into account. In this context, assessing the evolution of marine ecosystems is pivotal since they cover a large portion of the biosphere. For these reasons, we decided to develop an approach aimed at evaluating temporal and spatial dynamics of remotely-sensed chlorophyll a concentration. The concentrations of this pigment are linked with phytoplankton biomass and production, which in turn play a central role in marine environment. Machine learning techniques proved to be valuable tools in dealing with satellite data since they need neither assumptions on data distribution nor explicit mathematical formulations. Accordingly, we exploited the Self Organizing Map (SOM) algorithm firstly to reconstruct missing data from satellite time series of chlorophyll a and secondly to classify them. The missing data reconstruction task was performed using a large SOM and allowed to enhance the available information filling the gaps caused by cloud coverage. The second part of the procedure involved a much smaller SOM used as a classification tool. This dimensionality reduction enabled the analysis and visualization of over 37 000 chlorophyll a time series. The proposed approach provided insights into both temporal and spatial chlorophyll a dynamics in the Mediterranean Basin.