Retrievals of the main phytoplankton groups at Lake Constance using OLCI, DESIS, and evaluated with field observations

Kurzfassung

Phytoplankton play an important role in the aquatic biogeochemical cycling such as for the formation of organic matter by photosynthetic processes through the fixation of carbon dioxide, and assimilation of macro- and micronutrients depending on their metabolic needs. These processes are common to all phytoplankton, however some phytoplankton groups have specific needs and thus play different functional roles in the biogeochemical cycle, which are used to classify phytoplankton into different phytoplankton functional types (PFTs). Information on the phytoplankton groups can be obtained from satellite observations such as the Ocean and Land Colour Instrument (OLCI) onboard of ISS and Sentinel-3. PFTs global ocean abundance can be estimated based on the OC-PFT algorithm (Hirata et al. 2011 and related updates to it) which is based on the assumption that a marker pigment for a specific PFT varies in dependence to the chlorophyll-a concentration. In this study, OC-PFT retrieval has been developed and adapted for estimation of PFT from Lake Constance by using a large collection of in-situ HPLC data set measured since 2000 at the largest German inland water by the regional authority and further analysed to derive PFT using the diagnostic pigment analysis following Vidussi et al. (2001) with adapted coefficients for Lake Constance. The PFT retrieved from OLCI are validated using independent in situ data derived from HPLC pigment measurements from 4 field campaigns performed in 2019 and 2020 at Lake Constance. Concentrations for five phytoplankton groups (diatoms, dinoflagellates, cryptophytes, green algae, and prokaryotes) are retrieved for Lake Constance, being the dominants diatoms and cryptophytes and at lesser degree green algae. In addition, evaluation of synergistic PFT products are presented to enlarge the capabilities of PFT data in inland and coastal waters analytically retrieved from high spectral and high spatial data such as DESIS, EnMAP or PRISMA by synergistic use with OLCI OC-PFT data sets is discussed.