Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = ITCOMP-SOM

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
19 pages, 4092 KB  
Technical Note
Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning
by Agathe Puissant, Roy El Hourany, Anastase Alexandre Charantonis, Chris Bowler and Sylvie Thiria
Remote Sens. 2021, 13(8), 1445; https://doi.org/10.3390/rs13081445 - 8 Apr 2021
Cited by 14 | Viewed by 4459
Abstract
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, [...] Read more.
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. Full article
Show Figures

Graphical abstract

Back to TopTop