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Télédétection du Phytoplancton par Méthode Neuronale; Du Global au Régional, de la Composition Pigmentaire aux Biorégions

Roy El Hourany 1, 2
2 VARCLIM - Océan et variabilité du climat
LOCEAN - Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques
Abstract : This thesis presents a novel approach to analyze and observe the phytoplankton community structure at global and regional scale using satellite data (Ocean colour and Sea surface temperature) and in-situ observations. The approach is based on neural network classification methods, such as Self-Organizing Maps (SOM) trained on a large global database composed of satellite observations collocated with in-situ measurements. First, we developed a method to estimate secondary phytoplankton pigments from satellite measurements in the global ocean. Then we focused our studies on the Mediterranean Sea. Phytoplankton groups (PFTs) were identified from the secondary pigments estimated in the first phase. We then characterized seven bio-regions by clustering annual cycles MLD obtained from Argo floats, SST and Chla by using an advanced SOM. At last, these bio-regions were characterized in terms of PFTs. The methods developed in this thesis allowed us to estimate uncertainties on secondary pigments and PFTs. The applicability of these methods are broad and can be used to investigate other oceanic areas.
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Roy El Hourany. Télédétection du Phytoplancton par Méthode Neuronale; Du Global au Régional, de la Composition Pigmentaire aux Biorégions. Sciences de l'environnement. Sorbonne Université, 2019. Français. ⟨tel-02562426v1⟩

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