FN Archimer Export Format
PT J
TI Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning
BT 
AF Puissant, Agathe
   El Hourany, Roy
   Charantonis, Anastase Alexandre
   Bowler, Chris
   Thiria, Sylvie
AS 1:1;2:2;3:1,3;4:2;5:1,4;
FF 1:;2:;3:;4:;5:;
C1 Laboratoire d’Océanographie et du Climat Expérimentations et Approches Numériques (LOCEAN), Sorbonne Université, CNRS, IRD, MNHN, 75005 Paris, France
   Institut de Biologie de l’École Normale Supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Université, 75005 Paris, France
   École Nationale Supérieure d’Informatique pour l’Industrie et l’Entreprise (ENSIIE), 91000 Évry, France
   Observatoire de Versailles Saint-Quentin-en-Yvelins (OVSQ), Versailles Saint-Quentin-en-Yvelines University, 78280 Guyancourt, France
C2 UNIV PARIS 06, FRANCE
   ENS, FRANCE
   ENSIIE, FRANCE
   UNIV VERSAILLES ST QUENTIN EN YVELINES, FRANCE
IN DOAJ
IF 5.349
TC 4
UR https://archimer.ifremer.fr/doc/00698/80980/85012.pdf
LA English
DT Article
CR BIOSOPE
BO L'Atalante
DE ;machine learning;inversion;ocean colour;phytoplankton;pigment vertical profile;deep chlorophyll maximum;Tara Oceans;MAREDAT;pigments;ITCOMP-SOM;Self Organizing Maps
AB Observing the vertical dynamic of phytoplankton in the water column is essential to understand the evolution of the ocean primary productivity under climate change and the efficiency of the CO2 biological pump. This is usually made through in-situ measurements. In this paper, we propose a machine learning methodology to infer the vertical distribution of phytoplankton pigments from surface satellite observations, allowing their global estimation with a high spatial and temporal resolution. After imputing missing values through iterative completion Self-Organizing Maps, smoothing and reducing the vertical distributions through principal component analysis, we used a Self-Organizing Map to cluster the reduced profiles with satellite observations. These referent vector clusters were then used to invert the vertical profiles of phytoplankton pigments. The methodology was trained and validated on the MAREDAT dataset and tested on the Tara Oceans dataset. The different regression coefficients R2 between observed and estimated vertical profiles of pigment concentration are, on average, greater than 0.7. We could expect to monitor the vertical distribution of phytoplankton types in the global ocean.
PY 2021
PD APR
SO Remote Sensing
SN 2072-4292
PU MDPI AG
VL 13
IS 8
UT 000644666700001
DI 10.3390/rs13081445
ID 80980
ER

EF