Development and validation of an algorithm estimating primary production in the Southern North Sea

An algorithm to estimate daily primary production (PP algorithm) in the Southern North Sea from satellite and climatology data is described and tested. This algorithm – a wavelength-integrated but time- and depth-resolved photosynthesis model – includes three parameters: the maximal photosynthetic capacity PBmax, the photosynthetic efficiency aB and the vertical light attenuation coefficient Kd(PAR). These parameters are estimated from statistical models developed and validated on basis of experimentally determined photosynthetic parameters, PAR vertical profiles and relevant environmental data in the area. PBmax is derived from temperature and phosphate concentrations using a multiple linear regression model while aB is calculated from PBmax using a simple linear regression model. A natural logarithmic regression model taking into account the optically active components of these Case 2 waters, i.e. chlorophyll a (Chl a), suspended matter (SM) and dissolved coloured organic matter (CDOM approached by salinity) provides the best description of Kd(PAR). The three models have very good predictive capacity.The constructed PP algorithm is tested by using successively field, and MODIS-derived and climatology data (temperature, phosphate, Chl a, SM, salinity-derived CDOM) as inputs and the estimated daily PP is compared with in situ PP measured by traditional method. Results obtained for 17 stations constituting the validation data set are shown and the errors are estimated and discussed. Clearly the deviations between PP algorithm predictions and in situ PP are higher when using remote-sensing and climatology data due to errors on both parameterizations and the retrieved data. It is suggested that an approach combining remote sensing and hydrodynamical-ecological modeling would allow the PP-algorithm developed in this study to be fully functional in the Southern north Sea