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Phytoplankton is a diverse group of photosynthesizing organisms which account for approximately fifty percent of the primary production on Earth. Increasing our knowledge on phytoplankton dynamics (and plankton in general) is therefore of major importance. In the present research, we aimed to reveal the spatiotemporal dynamics of the phyto- and zooplankton community in the Eastern English Channel, Southern Bight of the North Sea and the Thames estuary. To do so, we organized a JERICO-NEXT Lifewatch cruise in May 2017 on board of the RV Simon Stevin and sampled 44 stations, involving five research institutions from France (CNRS-LOG,), The Netherlands (RWS, NIOZ) and Belgium (UGENT, VLIZ). To quantify the biomass of the phytoplankton community we used a unique combination of three flow cytometers and two Fast Repetition Rate Fluorometerss that were coupled to the underway ferrybox system. These observations were complemented with Water Insight Spectrometer and water profile data (by means of a CTD) and samples for zooplankton, pigment and nutrient analysis. A dedicated data workshop was organized with all partners to conduct a joint analysis on both the biotic and abiotic data. A first exploration of the data, by means of regression-based models and multivariate statistics, suggested that mainly nutrient discharges from the rivers influence the plankton structure. Furthermore, water turbidity is controlling photosynthetic activity and horizontal and vertical variations of photosynthetic properties can be discriminated.

Identification of phytoplankton groups from space is essential to map and monitor algal blooms in coastal waters, but remains a challenge due to the presence of suspended sediments and dissolved organic matter which interfere with phytoplankton signal. On the basis of field measurements of remote sensing reflectance (Rrs(lambda)), bio-optical parameters, and phytoplankton cells enumerations, we assess the feasibility of using multispectral and hyperspectral approaches for detecting spring blooms of Phaeocystis globosa (P. globosa). The two reflectance ratios (Rrs(490)/Rrs(510) and Rrs(442.5)/Rrs(490)), used in the multispectral inversion, suggest that detection of P. globosa blooms are possible from current ocean color sensors. The effects of chlorophyll concentration, colored dissolved organic matter (CDOM), and particulate matter composition on the performance of this multispectral approach are investigated via sensitivity analysis. This analysis indicates that the development of a remote sensing algorithm, based on the values of these two ratios, should include information about CDOM concentration. The hyperspectral inversion is based on the analysis of the second derivative of Rrs(lambda) (d lambda2 Rrs). Two criteria, based on the position of the maxima and minima of dlambda2 Rrs, are established to discriminate the P. globosa blooms from diatoms blooms. We show that the position of these extremes is related to the specific absorption spectrum of P. globosa and is significantly correlated with the relative biomass of P. globosa. This result confirms the advantage of a hyperspectral over multispectral inversion for species identification and enumeration from satellite observations of ocean color.