A Bayesian network-based modelling framework was proposed to predict the probability of exceeding critical thresholds for chlorophyll-a and turbidity in an Australian subtropical drinking water reservoir, based on Sentinel-2 data and prior knowledge. The model was trained with quasi-synchronous historical in situ and satellite data for 2018–2023 and achieved satisfactory accuracy (Brier score < 0.27 for all models) despite limited poor water quality events in the final dataset. The graphical output of the model (posterior probability maps of high turbidity or chlorophyll-a) provides an effective means for the user to evaluate both the prediction, and the uncertainty behind the predictions in a single map. This avoids loss of trust in the model and can trigger spatially targeted data collection in order to reduce uncertainty. Future work will focus on refining the modelling methodology and its automation, as well as including other data such as in situ high-frequency sensors.