Eutrophication processes in coastal waters are becoming more prominent as a result of high nutrient discharges from intensive agriculture and increased urban waste. These processes can be devastating for local ecosystems and lead to dissolved oxygen depletion, which applies considerable stress on aquatic organisms. For ecosystems to preserve their status, stop and reverse the negative effects of eutrophication, regular estimation of corresponding indicators has to take place. In this direction, mostly process-driven models have been used, but the presented project argues that freely available remote sensing data can also provide useful insights for the oxygen saturation of the water. The proposed methodology uses Sea Surface Temperature and Chlorophyll-a estimations from AQUA and ENVISAT satellite sensors for the period 2003-2011 to predict the dissolved oxygen content in the Dutch coastal waters. It does so by implementing various Machine Learning models, namely Random Forest, Artificial Neural Network and Gradient Boosting Regressors, with the latter demonstrating the best results. After extensive data pre-processing, the results show that dissolved oxygen can be predicted with an average Root-Mean-Squared error of 0.8 g/m3. Important steps towards a lower error include the use of gap-filled variables and their decomposition into their temporal components as inputs for the model. Furthermore, the effect of the Sea Surface Temperature on the dissolved oxygen is documented through its contribution in the estimation of the latter’s seasonal variability, while the estimation of the maximum dissolved oxygen values is attributed to Chlorophyll-a. Further feature engineering and model development can possibly improve the estimation of the minimum dissolved oxygen values in the coast and the overall prediction in more complex intertidal areas, like the Wadden Sea.