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, 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.
Highlights
Phytoplankton is a key player in ocean biodiversity with consequences on fish catch potential, and climate regulation through carbon dioxide storage [1,2,3,4]
We introduce a new machine learning (ML) methodology to estimate several phytoplankton pigment profiles from ocean-color data, hindering a multidimensional problem based on the co-estimation of six different pigments
In this paper, robust estimations of the vertical variability of six phytoplankton pigments (Chla, fucox, 19hex, perid, zeax and DVChla) from the surface to a depth of 300 m, using satellite surface measurements at high spatial and temporal resolution
Summary
Phytoplankton is a key player in ocean biodiversity with consequences on fish catch potential, and climate regulation through carbon dioxide storage [1,2,3,4]. A decline in total phytoplankton population has been observed in Northern hemisphere basins over the last decade [5] and is projected to strengthen over the 21st century over wide oceanic regions under all global warming scenarios [6]. This decline is one of the most alarming consequences of anthropogenic climate change, as highlighted by recent policy-relevant reports [7] and by a scientists’ warning to a humanity consensus statement in Nature. An important question is how phytoplankton composition responds to changes in ocean characteristics (temperature, nutrients, currents, stratification, ...) since phytoplankton diversity constrains the societal impacts on both climate and fisheries
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