The effect of satellite sea surface temperature assimilation on the forecast quality of the coastal ocean-biogeochemical model HBM-ERGOM in the North- and Baltic Seas is studied. The HBM-ERGOM model is currently used operationally without data assimilation by the German Federal Maritime and Hydrographic Agency (BSH). The model is configured with nested grids with a resolution of 5 km in the North- and Baltic Seas and a resolution of 900 m in the German coastal waters. The biogeochemical model ERGOM contains three phytoplankton groups (Cyanobacteria, Flagellates, Diatoms) and two zooplankton size groups to simulated the biogeochemical cycling in the coastal seas. To improve the predictions of the HBM-ERGOM model, data assimilation was added by coupling the model to the parallel data assimilation framework (PDAF, http://pdaf.awi.de). The ensemble-based error-subspace transform Kalman filter (ESTKF) is applied for the data assimilation. As a first step to improve the biogeochemical forecasts, before the planned assimilation of ocean color data products, the impact of assimilating satellite sea surface temperature data is assessed. Two cases are considered. First, the impact of weakly coupled data assimilation. In this case, the assimilation of temperature only directly influences the physical model variables in the analysis step while the biogeochemical fields react dynamically to the changed physical model state during the ensemble forecasts using the coupled model. The second case is the strongly-coupled data assimilation in which next to the physical model fields also the biogeochemical fields are directly updated in the analysis step through the multivariate covariances estimated by the joined physical-biogeochemical ensemble of model states. Here, it is assessed whether these covariances are sufficiently well estimated to result in an improvement of the biogeochemical fields.