Reproduction of Spatio-Temporal Patterns of Major Mediterranean Phytoplankton Groups from Remote Sensing OC-CCI Data

Gabriel Navarro,Isabel Caballero,Pablo Almaraz,Isabel E Huertas,Águeda Vázquez

doi:10.3389/fmars.2017.00246

Abstract

During the last two decades, several satellite algorithms have been proposed to retrieve information about phytoplankton groups using ocean colour data. One of these algorithms, the so-called PHYSAT-Med, was developed specifically for the Mediterranean Sea due to the optical peculiarities of this basin. The method allows the detection from ocean colour images of the dominant Mediterranean phytoplankton groups, namely nanoeukaryotes, Prochlorococcus, Synechococcus, diatoms, coccolithophorids and Phaeocystis-like phytoplankton. Here, we present a new version of PHYSAT-Med applied to the Ocean Colour – Climate Change Initiative (OC-CCI) database. The OC-CCI database consists of a multi-sensor, global ocean-colour product that merges observations from four different sensors. This retuned version presents improvements with respect to the previous version, as it increases the temporal range (since 1998), decreases the cloud cover, improves the bias correction and a validation exercise was performed in the NW Mediterranean Sea. In particular, the PHYSAT-Med version has been used here to analyse the annual cycles of the major phytoplankton groups in the Mediterranean Sea. Wavelet analyses were used to explore the spatial variability in dominance both in the time and frequency domains in several Mediterranean sub-regions, such as the Alboran Sea, Ligurian Sea, Northern Adriatic Sea and Levantine basin. Results extended the interpretation of previously detected patterns, indicating the dominance of Synechococcus-like versus prochlorophytes throughout the year at the basin level, and the predominance of nanoeukaryotes during the winter months. The method successfully reproduced the diatom blooms normally detected in the basin during the spring season (March to April), especially in the Adriatic Sea. According to our results, the PHYSAT-Med OC-CCI algorithm represents a useful tool for the spatio-temporal monitoring of dominant phytoplankton groups in Mediterranean surface waters. The successful applications of other regional ocean colour algorithms to the OC-CCI database will give rise to extended time series of phytoplankton functional types, with promising applications to the study of long-term oceanographic trends in a global change context.

Highlights

Since the launch of the Coastal Zone Color Scanner (CZCS) in the late 1970s, ocean color remote sensing has deeply improved our understanding of the ocean system by providing global estimations of the surface chlorophyll concentration (Chla), a parameter known to be a good proxy of phytoplankton biomass (e.g., McClain, 2009)
During the last 40 years, observations of regional-to-global Chla data have been acquired by different ocean color sensors (IOCCG, 2012), such as Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), MediumResolution Imaging Spectrometer (MERIS) and Visible Infrared Imager Radiometer Suite (VIIRS)
The maxima found by the PHYSAT-Med Ocean Colour—Climate Change Initiative (OC-CCI) algorithm were in close agreement with the maxima in the concentrations of the pigment ratio for Divinyl Chlorophyll-a (dChla) measured by high-performance liquid chromatography (HPLC) method, which is indicative of prochlorophytes (Goericke and Repeta, 1992; Claustre and Marty, 1995; Vidussi et al, 2001)

Summary

Introduction

Since the launch of the Coastal Zone Color Scanner (CZCS) in the late 1970s, ocean color remote sensing has deeply improved our understanding of the ocean system by providing global estimations of the surface chlorophyll concentration (Chla), a parameter known to be a good proxy of phytoplankton biomass (e.g., McClain, 2009). In order to extend the existing time series beyond that provided by a single satellite sensor, the European Space Agency (ESA) has recently generated the Ocean Colour—Climate Change Initiative (OC-CCI), a multisensor, global, ocean-color product mainly devoted to climate research (Storm et al, 2013) that merges observations from four different sensors: SeaWiFS, MODIS, MERIS, and VIIRS. The main reason behind this choice is that SeaWiFS is widely considered as the highest quality sensor with the best match to in situ observations, and is commonly used in peer literature (Couto et al, 2016) This dataset improves the bias correction, reducing the sensitivity to medium-term changes and extending the method applicability beyond the lifetime of SeaWiFS. The current OC-CCI database allows for the examination of the spatial and temporal variability of surface Chla since September 1997 (Couto et al, 2016)

Objectives

Methods

Results

Conclusion