Mapping and predicting crop yield on a large scale is increasingly important for use cases such as policy-making, risk insurance and precision agriculture applications at farm and field scale. The higher spatial resolution of Sentinel-2 compared to Landsat allows for satellite-based crop yield mapping even in relatively small scaled agricultural settings such as found in Switzerland and other central European regions. In this study, five years (2017–2021) of cereal crop yield data from a combine harvester were used to model crop yield within-field, on a spatial scale corresponding to the Sentinel-2 pixel level. Three established methods from literature using (i-ii) spectral indices and (iii) raw satellite reflectance as well as (iv) a recurrent neural network (RNN) were chosen for analysis. Although the RNN approach did not outperform the other methods, it was more efficient because of the comparatively simple end-to-end training of the model, resulting in much less time spent on data cleaning and feature extraction needed for spectral index time series analysis. The RNN was also able to discriminate cloudy data by itself, reaching similar performance levels as if using pre-processed, cloud-free data. Modelling was performed on individual years, all years combined and on unseen years using leave-one-year-out cross-validation. The models performed best when using data from all years (R2 up to 0.88, relative RMSE up to 10.49 %) and showed poor performance when predicting on unseen data years, especially for years with previously unknown weather patterns. This highlights the importance of yearly model calibration and the need for continuous data collection enabling long time series for future crop yield models.