In order to understand the marine phytoplankton’s role in the global marine ecosystem and biogeochemical cycles it is necessary to derive its global, in particular the distribution of major functional phytoplankton types (PFT), but also on phytoplankton physiology and its degradation products in the world oceans. Nearly all current global ocean colour products are retrieved from multi-spectral ocean colour sensors, because of their much higher spatial resolution and temporal coverage, but as well for the lack of expertise to analyze hyper-spectral datasets. The use of multi-spectral data limits the ability to differentiate among the optical imprints of different water constituents. Although phytoplankton types have different marker pigments, the differences in the spectral absorption structures are small, since they also have many pigments in common. The small number of wavelength bands and the broad band resolution of multi-spectral sensors provide only limited information on the difference of the phytoplankton absorption structures.

To get a global quantitative estimate of different PFT in the oceans, PHYTOOPTICS in cooperation with the Institute of Environmental Physics at the University of Bremen (IUP) has adapted the technique of Differential Optical Absorption Spectroscopy (DOAS), which has been established for retrieval of atmospheric components, for the retrieval of the absorption and biomass of major phytoplankton groups (PhytoDOAS; Phytoplankton DOAS) from data of the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY; SCanning Imaging absorption spectrometer for Atmospheric ChartographY onboard ENVISAT) satellite sensor. It allows the determination of the biomass of the four above mentioned different phytoplankton groups (Bracher et al. 2009 describes the retrieval of two PFT, Sadeghi et al. 2012a the improved PhytoDOAS with the retrieval of four PFT, see Figure 1 below) analytically and independent from a priori information using high spectrally resolved satellite data from SCIAMACHY. PhytoDOAS accounts in addition to atmospheric compounds also for the absorption of water itself and its optical constituents. So far, the method has only been applied (e.g. in Sadeghi et al. 2012b, Ye et al. 2012) to the hyper-spectral SCIAMACHY data (covering 2002-2012). The drawback is the coarser spatial resolution of the ground scene (30 km by 60 km) which is nonetheless acceptable for open ocean conditions. However, the coverage close to the coasts and at the high latitudes very limited.

Especially at coastal sites a data product with better temporal and spatial resolution is required in order meet the data users’ needs. To tackle this short-coming, we have conducted, funded by ESA, the SynSenPFT project were a high spatially and temporal resolved data set of phytoplankton groups from space was developed (Losa et al. 2017). Specific adaptions of PhytoDOAS to other and new hyperspectral sensors have been performed lately (Oelker et al. 2019, DFG-project PHYSYN ) or are underway (ESA project  S5POC). In addition lately we have been succesful to also obtain globally 5-6 different phytoplankton groups chl-a concentration from multispectral data (Xi et al. 2020, more details in ACRI-AWI project OLCI-PFT). For work within the project SynSenPFT,  S5POC and OLCI-PFT on determining the pixel by pixel uncertainty and merging the different data sets also techniques derived from data assimilation are used.

Additionally, hyper-spectral satellite data and methods like PhytoDOAS have the potential to overcome this: the optical imprints of different “types” (terrigenous or marine) can be observed by the filling-in of Fraunhofer Lines in the hyper-spectral backscattered sun light due to the specific trans-spectral processes (Wolanin et al. 2015a, 2015b - see details in PhD project A. Wolanin), as it can be detected for inelastic scattering on water molecules (Vountas et al. 2007, Dinter et al. 2015, Oelker et al. 2019). In addition, chl-a fluorescence provides rich source of physiological information of phytoplankton because of the inverse relationship of chl-a fluorescence and photosynthesis.

All data have been and still are used to improve coupled ecosystem-ocean modelling in order to assess variability and change of PFT in this ocean (Losa et al. 2019, Pefanis et al. 2020 - see also OZE modelling webpage).

References and Publications

See group's peer-reviewed publications for: Blum et al. 2012, Dinter et al. 2015, Wolanin et al. 2015a, Wolanin et al. 2015b, Oelker et al. 2019, Soppa et al. 2019