Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning

Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning

Blog Article

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 Beyond the Score: Timbre Analysis in Avant-garde Music 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 AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models 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.