Abstract
This study presents an algorithm for globally retrieving chlorophyll a (Chl-a) concentrations of phytoplankton functional types (PFTs) from multi-sensor merged ocean color (OC) products or Sentinel-3A (S3) Ocean and Land Color Instrument (OLCI) data from the GlobColour archive in the frame of the Copernicus Marine Environmental Monitoring Service (CMEMS). The retrieved PFTs include diatoms, haptophytes, dinoflagellates, green algae and prokaryotic phytoplankton. A previously proposed method to retrieve various phytoplankton pigments, based on empirical orthogonal functions (EOF), is investigated and adapted to retrieve Chl-a concentrations of multiple PFTs using extensive global data sets of in situ pigment measurements and matchups with satellite OC products. The performance of the EOF-based approach is assessed and cross-validated statistically. The retrieved PFTs are compared with those derived from diagnostic pigment analysis (DPA) based on in situ pigment measurements. Results show that the approach predicts well Chl-a concentrations of most of the mentioned PFTs. The performance of the approach is, however, less accurate for prokaryotes, possibly due to their general low variability and small concentration range resulting in a weak signal which is extracted from the reflectance data and corresponding EOF modes. As a demonstration of the approach utilization, the EOF-based fitted models based on satellite reflectance products at nine bands are applied to the monthly GlobColour merged products. Climatological characteristics of the PFTs are also evaluated based on ten years of merged products (2002−2012) through inter-comparisons with other existing satellite derived products on phytoplankton composition including phytoplankton size class (PSC), SynSenPFT, OC-PFT and PHYSAT. Inter-comparisons indicate that most PFTs retrieved by our study agree well with previous corresponding PFT/PSC products, except that prokaryotes show higher Chl-a concentration in low latitudes. PFT dominance derived from our products is in general well consistent with the PHYSAT product. A preliminary experiment of the retrieval algorithm using eleven OLCI bands is applied to monthly OLCI products, showing comparable PFT distributions with those from the merged products, though the matchup data for OLCI are limited both in number and coverage. This study is to ultimately deliver satellite global PFT products for long-term continuous observation, which will be updated timely with upcoming OC data, for a comprehensive understanding of the variability of phytoplankton composition structure at a global or regional scale.
Highlights
Satellite ocean color (OC) remote sensing has been widely used for estimating chlorophyll a (Chl-a) concentration, which is often used as an indicator of phytoplankton biomass
We aim firstly to establish the empirical orthogonal functions (EOF) fitted model based on the nearly globally covered matchups between the satellite Rrs and the phytoplankton functional types (PFTs) Chl-a concentrations derived from diagnostic pigment analysis (DPA) of in situ HPLC pigment data, and cross-validate the performance of the EOF-based algorithm statistically; secondly, to set up the PFT retrieval scheme based on the EOF modes obtained from the matchups for the implementation to satellite OC products; thirdly, to investigate and evaluate the climatological characteristics of the PFTs retrieved from merged OC products (2002–2012) through inter-comparisons with other existing PFT/phytoplankton size class (PSC) products at the same period, and to explore the potential of applying the approach to Ocean and Land Color Instrument (OLCI) products based on a prediction scheme using a much more limited number of matchups
With hyperspectral Rrs, a few bio-optical features related to phytoplankton pigments and to PFTs can be caught only when they are prominent enough, such as phycocyanin which causes an obvious trough in 620–630 nm
Summary
Satellite ocean color (OC) remote sensing has been widely used for estimating chlorophyll a (Chl-a) concentration, which is often used as an indicator of phytoplankton biomass. We seek to establish an approach that uses satellite reflectance data which inherit the information of various phytoplankton pigments and, allows retrieving the Chl-a concentrations of multiple PFTs. We choose the empirical orthogonal function (EOF) analysis, known as principal component analysis, as it has been previously used for predicting ocean color metrics and various phytoplankton pigment concentrations by assessing variance of structures in spectral remote sensing reflectance (Rrs) or water leaving radiance (e.g., Lubac and Loisel, 2007; Craig et al, 2012; Taylor et al, 2013; Bracher et al, 2015; Soja-Woźniak et al, 2017). We aim firstly to establish the EOF fitted model based on the nearly globally covered matchups between the satellite Rrs and the PFT Chl-a concentrations derived from diagnostic pigment analysis (DPA) of in situ HPLC pigment data, and cross-validate the performance of the EOF-based algorithm statistically; secondly, to set up the PFT retrieval scheme based on the EOF modes obtained from the matchups for the implementation to satellite OC products; thirdly, to investigate and evaluate the climatological characteristics of the PFTs retrieved from merged OC products (2002–2012) through inter-comparisons with other existing PFT/PSC products at the same period, and to explore the potential of applying the approach to OLCI products based on a prediction scheme using a much more limited number of matchups
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