Accurate crop yield predictions play a crucial role in enabling informed policy-making to ensure food security. Beyond using advanced methods such as remote sensing and data assimilation (DA), it is essential to comprehend the influence of various sources of uncertainty on the overall prediction uncertainty. This study presents a novel approach for enhancing the accuracy of crop yield predictions by assimilating remotely-sensed Leaf Area Index (LAI) and updating weather ensemble data into a crop model (SPASS) while accounting for calibration and weather uncertainty. In addition, we investigated the effect of model calibration prior to DA by four calibration data type scenarios. These scenarios involve calibrating the crop model to different combinations of yield, phenology, and LAI, ranging from minimum (yield only) to maximum (yield, phenology, and LAI) data availability. To address weather uncertainty, we derived weather forecasts downscaled from climate models utilizing the MarkSim weather generator. Our results demonstrate that the assimilation LAI and updating weather data significantly reduces the overall uncertainty in crop yield predictions. Notably, the uncertainty associated with weather ensembles has a more substantial influence compared to the uncertainty resulting from calibration. This finding highlights the significance of accounting for variations and discrepancies in weather predictions when assessing yield uncertainty. Additionally, given the set of SPASS model parameters used for winter wheat calibration, additional field-based LAI data does not improve the calibration quality. •Assimilating LAI and weather data improves yield predictions by the SPASS model.•Calibrating SPASS to phenology and yield data adequately primes it for assimilation.•SPASS calibration to LAI does not enhance the efficacy of LAI assimilation.•Bias correction in weather data during assimilation markedly reduces yield prediction bias.•Weather uncertainty outweighs parameter uncertainty in yield predictions.