Abstract
ABSTRACT The Atlantic coast of Morocco, being part of the Easter Boundaries Upwelling Ecosystem, is characterized by high biological productivity and seasonally variable upwelling all year around. In this work, we develop new deep learning tools to monitor the Moroccan upwelling from biological and physical satellite images. The proposed method consists of a convolutional neural network (CNN) based on an encoder-decoder built on the U-Net structure, to localize the upwelling regions. Furthermore, we provide a new indices based on the analysis of sea surface temperature (SST) and chlorophyll-a (chl-a) images to give an overall view of the upwelling variability from both the biological and physical sides at once. The new proposed indices are based on the proposed segmentation method, which makes it possible to monitor the upwelling dynamics from both satellite observations. The elaborated procedure is applied over a database of weekly SST and chl-a images covering the period from 2000 to 2019, and the results are used to analyse its fluctuations between seasonal and interannual variations in the region.
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