Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data

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

Assessing bio-physical feedbacks in the shelf areas of Laptev Sea

In the context of climate change and of thawing permafrost in Siberia, the freshwater and organic material supplied by rivers to the Arctic Ocean, may increase heavily in the future. Here, we investigate the effect of the variability of optically active water constituents on the heat budget of the Laptev Sea surface waters. As a first step, we simulate the radiative heating with coupled atmosphere-ocean radiative transfer modelling (RTM). By using satellite remote sensing retrievals of Coloured Dissolved Organic Matter (CDOM), Total Suspended Matter (TSM), Chlorophyll-a (Chla) and sea surface temperature data as input to the RTM simulations, we present the spatial distribution of potential radiative heating of Laptev Sea shelf areas. Additionally, an ocean biogeochemical model coupled to a general circulation model is used to simulate the dynamics of various constituents in response to Arctic Amplification and the feedback on surface heating and sea ice melting. Results suggest that high concentration of CDOM, TSM and Chla in Arctic waters increase the heating rate at the surface of the ocean and reduce the heat losses to the atmosphere during summer. The induced surface heating can result to higher ice melting rates with potential implications to upper ocean stratification and primary production

Investigating the Arctic phytoplankton variability and diversity based on modeling and satellite retrievals

In our study we focus on improving our understanding of possible interactions between the open water and sea ice and the surface ocean biogeochemistry under the recently observed sea ice decline in the Arctic. In particular, the analysis of changes in phytoplankton functional types (PFTs) over 2002 to 2012 based on long-term time series of satellite retrievals and supported by a modeling study is presented. The phytoplankton dynamics as well as phytoplankton diversity in response to Arctic Amplification is simulated with the DARWIN biogeochemical model (Follows et al., 2007, Dutkiewicz et al., 2015) coupled to the Massachusetts Institute of Technology general circulation model (MITgcm) with a configuration based on a cubed‐sphere grid (Menemenlis et al. 2008). The model results are complemented with information on phytoplankton compositions retrieved with PhytoDOAS (Bracher et al. 2009, Sadeghi et al. 2012) from available hyper-spectral optical satellite measurements (SCIAMACHY and OMI), which are synergistically combined via an optimal interpolation technique with multi-spectral optical satellite data (OC-CCI)

Global Retrieval Algorithms for Phytoplankton Functional Types (PFTs): toward the Applications to OLCI and GlobColour Merged Products

With integrated use of extensive in situ measurements from various cruises in different regions, this study focuses on PFT retrieval algorithms that are then applied to Sentinel-3 (S3) OLCI data and merged ocean colour (OC) products from CMEMS GlobColour archive. The main retrieved PFTs include diatoms, haptophytes, prokaryotic phytoplankton (cyanobacteria). Previously investigated retrieval methods, empirical orthogonal functions (EOF) for pigment concentrations estimation (Bracher et al. 2015) and generalized IOP (GIOP) (Werdell et al. 2013, 2014) for PFT discrimination, are tested and adapted potentially with full use of our current available in situ measurements from various campaigns worldwide, in which we have a number of collocated remote sensing reflectance spectra (Rrs) and PFT data based on HPLC pigments in addition to other bio-optical measurements. Algorithms are tested and compared by both taking hyperspectral and multispectral in situ Rrs spectra as input data, and the multispectral based approach is later on applied to the above mentioned satellite data. Performances of both EOF- and GIOP-based approaches are assessed statistically and cross-validated, with results showing that both could well predict chlorophyll-a concentrations for diatoms and haptophytes but less good for prokaryotes. In a next step these algorithms are adapted to satellite OC data collocated to an even larger in-situ PFT database derived from HPLC phytoplankton pigments. This is to eventually develop the global satellite PFT products for long-term observation, updated timely with more available OLCI data in the future, and intercompared to the results with other existing PFT products (e.g. PhytoDOAS, OC-PFT, SynSenPFT)

Untersuchung von Phytoplanktonänderungen aus Fernerkundungsdaten im Südpolarmeer

The Southern Ocean (SO) is responsible for about half of the global annual uptake of anthropogenic carbon dioxide from the atmosphere. As a remote region, satellite remote sensing is a cost-effective alternative to investigate and gain additional insights into the current knowledge of phytoplankton dynamics in this region. This thesis was set out to investigate ocean colour retrievals and phytoplankton dynamics in the SO and it was developed as a multidisciplinary work using in situ and remote sensing data. The studies developed here have moved forward our knowledge of ocean colour in the SO and contributed to a better understanding of the ocean biogeochemical cycle from the ocean colour perspective by adding new information on the uncertainties in the input terms of primary production models, on the estimation of diatoms abundance and on the variability of diatoms phenology

Remote sensing of coccolithophore blooms in selected oceanic regions using the PhytoDOAS method applied to hyper-spectral satellite data.

In this study temporal variations of coccolithophore blooms are investigated using satellite data. Eight years, from 2003 to 2010, of data of SCIAMACHY, a hyper-spectral satellite sensor on-board ENVISAT, were processed by the PhytoDOAS method to 5 monitor the biomass of coccolithophores in three selected regions. These regions are characterized by frequent occurrence of large coccolithophore blooms. The retrieval results, shown as monthly mean time-series, were compared to related satellite products, including the total surface phytoplankton, i.e., total chlorophyll-a (from GlobColour merged data) and the particulate inorganic carbon (from MODIS-Aqua). The 10 inter-annual variations of the phytoplankton bloom cycles and their maximum monthly mean values have been compared in the three selected regions to the variations of the geophysical parameters: sea-surface temperature (SST), mixed-layer depth (MLD) and surface wind speed, which are known to affect phytoplankton dynamics. For each region the anomalies and linear trends of the monitored parameters over the period of this 15 study have been computed. The patterns of total phytoplankton biomass and specific dynamics of coccolithophores chlorophyll-a in the selected regions are discussed in relation to other studies. The PhytoDOAS results are consistent with the two other ocean color products and support the reported dependencies of coccolithophore biomass’ dynamics to the compared geophysical variables. This suggests, that PhytoDOAS 20 is a valid method for retrieving coccolithophore biomass and for monitoring its bloom developments in the global oceans. Future applications of time-series studies using the PhytoDOAS data set are proposed, also using the new upcoming generations of hyper-spectral satellite sensors with improved spatial resolution

Intercomparison of ocean color products identifying coccolithophore blooms on global and regional scales.

Nearly ten years (July 2002 to April 2012) of SCIAMACHY data, a hyper-spectral satellite sensor on-board ENVISAT, were processed by the improved, multi-target, PhytoDOAS method to monitor the biomass of coccolithophores besides diatoms and cyanobacteria. Data have been evaluated with other coccolithphore related satellite products and modeled coccolithophore distributions derived from the NASA Ocean Biogeochemical Model. The retrieval's sensitivity was assessed by using the coupled oceanic-atmospheric radiative transfer model SCIATRAN. Temporal variations of coccolithophores were investigated using satellite data in three selected regions characterized by frequent occurrence of large coccolithophore blooms. Monthly mean data were compared to related satellite products, including the total surface phytoplankton, i.e., total chlorophyll-a (from GlobColour merged data) and the particulate inorganic carbon (from MODIS-Aqua). In addition, the inter-annual variations of the phytoplankton bloom cycles and their maximum monthly mean values were compared in the three selected regions to the variations of the following geophysical parameters: sea-surface temperature (SST), mixed-layer depth (MLD) and surface wind speed, which are known to affect phytoplankton dynamics. PhytoDOAS data are consistent with the two other ocean color products and support the reported dependencies of coccolithophore biomass' dynamics to the compared geophysical variables. These results suggest that multi-target PhytoDOAS is a valid method for retrieving coccolithophores' biomass and for monitoring their bloom developments in the global oceans