The effect of the assimilation of satellite sea surface temperature onto 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 pre-operationally, without data assimilation, by the Germany Federal Maritime and Hydrographic Agency (BSH). The model is configured with nested grids with a resolution of 5km in the North and Baltic Seas and a resolution of 900m in the German coastal waters. 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 local error-subspace transform Kalman filter (LESTKF) is applied for the data assimilation. It is studied how the biogeochemical model fields are impacted by the assimilation of sea surface temperature (SST) data from the Sentinel 3a and NOAA satellites. 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. For the weakly-coupled assimilation it is found that while the biogeochemical model fields are influenced by the SST data assimilation, the averaged deviation from in situ data remains almost constant and small improvements, but also deterioration, can occur. In case of the strongly coupled assimilation, the vertical effect of the assimilation has to be constrained to avoid deterioration. This effect is caused by the ensemble-estimated correlations between SST and biogeochemical fields in lower layers of the model.