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

Abstract

During the last two decades, several satellite algorithms have been proposed to retrieve information about phytoplankton groups using ocean color 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 color 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-color 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 vs. 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 color 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.

Phytoplankton Diversity in the Mediterranean Sea From Satellite Data Using Self‐Organizing Maps

“…The spatiotemporal variability of the dominant PFTs in the Mediterranean Sea observed in this study highlights the dominance of Hapto and Chloro in winter-spring seasons essentially in the western basin, whereas Synechococcus dominate most of the Mediterranean waters during summer. These results agree with previous studies on the Mediterranean Sea (e.g., Navarro et al, 2017;Sammartino et al, 2015;Uitz et al, 2012;Vidussi et al, 2000Vidussi et al, , 2001.…”

“…Journal of Geophysical Research: Oceans Navarro et al (2014Navarro et al ( , 2017 developed a regionalized version of PHYSAT (PHYSAT-Med), which is based on the OC-CCI merged ocean color data. Originally, PHYSAT identifies dominant phytoplankton types using normalized reflectance anomalies.…”

“…This was done by using relationship between Chla and PSC Hirata et al, 2008;Mouw & Yoder, 2010;Uitz et al, 2006) and by taking into account the spectral information (Alvain et al, 2005;Ben Mustapha et al, 2013;Sathyendranath et al, 2014). Some of these algorithms are based on spectral features, such as backscattering (Kostadinov et al, 2009), absorption (Bracher et al, 2009;Ciotti & Bricaud, 2006;Mouw & Yoder, 2010;Roy et al, 2013), or a hybrid between absorption and backscattering (Fujiwara et al, 2011), while other algorithms exploit second-order anomalies of water leaving radiance such as PHYSAT (Alvain et al, 2005(Alvain et al, , 2008Ben Mustapha et al, 2013;Navarro et al, 2014Navarro et al, , 2017. The PHYSAT method is based on the identification of specific signatures in the normalized water leaving radiance spectra measured by an ocean color sensor after removal the impact of Chla variations.…”

“…Applied to oceanic Case I water, this method is designed to detect satellite pixels in which the dominant groups are nanoeukaryotes (and separately Phaeocystis‐like and coccolithophores), two types of picoplankton ( Prochlorococcus ‐ and Synechococcus ‐like cyanobacteria) and diatoms (Alvain et al, ). Furthermore, a regionalized version of the PHYSAT method has been specifically developed for the Mediterranean Sea due to the peculiarities of phytoplankton assemblages and succession than can be found in this basin and its particular optical properties (Navarro et al, , ).…”

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

“…In the Mediterranean Sea, regional ocean color algorithms and products were used to characterize the spatialtemporal variability of PFTs (Di Cicco et al, 2017;Navarro et al, 2014Navarro et al, , 2017Sammartino et al, 2015;Uitz et al, 2012;Volpe et al, 2007). Observation-based analysis showed that small-sized groups, either PIC (Magazzù & Decembrini, 1995) or NNP (Uitz et al, 2012), can dominate the primary production in the Mediterranean Sea, although MIC production can also be relevant in some spring-blooming Mediterranean areas (Uitz et al, 2012).…”