Climate change is a threat to many high-latitude regions. Changing patterns in precipitation intensity and increasing glacial ablation during spring and summer have major influence on river dynamics and the risk of widespread flooding. To monitor these rapid events, more frequent discharge observations are necessary. Having access to near-daily satellite based discharge observations is therefore highly beneficial. In this context, the recently launched Sentinel-1 and 2 satellites promise unprecedented potential, due to their capacity to obtain radar and optical data at high spatial (10 m) and high temporal (1–3 days) resolutions. Here, we use both missions to provide a novel approach to estimate the discharge of the Þjórsá (Thjórsá) river, Iceland, on a near-daily basis. Iceland, and many other high-latitude regions, are affected by frequent cloud-cover, limiting the availability of cloud-free optical Sentinel-2 data. We trained a Random Forest supervised machine learning classifier with a set of Sentinel-1 backscatter metrics to classify water in the individual Sentinel-1 images. A Sentinel-2 based classification mask was created to improve the classification results. Second, we derived the river surface area and converted it to the effective width, which we used to estimate the discharge using an at-a-station hydraulic geometry (AHG) rating curve. We trained the rating curve for a six-month training period using in situ discharge observations and assessed the effect of training area selection. We used the trained rating curve to estimate discharge for a one-year monitoring period between 2017/10 and 2018/10. Results showed a Kling-Gupta Efficiency (KGE) of 0.831, indicating the usefulness of dense Sentinel-1 and 2 observations for accurate discharge estimations of a medium-sized (200 m width) high-latitude river on a near-daily basis (1.56 days on average). We demonstrated that satellite based discharge products can be a valuable addition to in situ discharge observations, also during ice-jam events.