Abstract

Past years have seen the development of different approaches to detect phytoplankton groups from space. One of these methods, the PHYSAT one, is empirically based on reflectance anomalies. Despite observations in good agreement with in situ measurements, the underlying theoretical explanation of the method is still missing and needed by the ocean color community as it prevents improvements of the methods and characterization of uncertainties on the inversed products. In this study, radiative transfer simulations are used in addition to in situ measurements to understand the organization of the signals used in PHYSAT. Sensitivity analyses are performed to assess the impact of the variability of the following three parameters on the reflectance anomalies: specific phytoplankton absorption, colored dissolved organic matter absorption, and particles backscattering. While the later parameter explains the largest part of the anomalies variability, results show that each group is generally associated with a specific bio-optical environment which should be considered to improve methods of phytoplankton groups detection.

Highlights

  • For a given chlorophyll a concentration (Chl a), phytoplankton groups scatter and absorb light differently according to their pigments composition, shape and size

  • We focus on the effect of phytoplankton absorption, aphy, particulate backscattering coefficient, bbp, and absorption by colored dissolved organic matter, acdom

  • After a necessary first step of development and validation of the PHYSAT method, a theoretical explanation of the empirical anomalies was strongly needed in order to move forward in the domain of phytoplankton groups detection

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Summary

Introduction

For a given chlorophyll a concentration (Chl a), phytoplankton groups scatter and absorb light differently according to their pigments composition, shape and size. The first order signal retrieved from ocean color sensors in open oceans, the normalized water leaving radiance (nLw), is due to Chl a [1,2]) and cannot be used to extract information about phytoplankton groups present in the oceanic surface layer. To circumvent this difficulty, different approaches have been developed in the past few years. Note that ‘phytoplankton groups’ are defined here following the definition based on functional types, as detailed in a previously published article [6]

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