The term ”Algal Bloom” refers to the accumulation of algae in a confined geological space. They may harm human health and negatively affect ecological systems around the area. Thus, forecasting algal blooms could mitigate the environmental and socio-economical damages. Particularly, the use of deep learning methods could distinguish underlying patterns such as spatio-temporal dependencies in the available remote sensing data of environmental factors that might cause algal blooms, such as change in water temperature. This research paper will aim to answer the following research question: Does the inclusion of explicit spatio-temporal embedding methods display a significant improvement for predicting algal blooms? The paper will use the UNet architecture and further encodes spatial and temporal information to be explicitly included as features in the training and validation process of deep learning models. The results of the experiment show that the inclusion of explicit spatio-temporal information separately into the feature space exhibits a small increase in performance. However, the combination of spatio-temporal information does not display a significant improvement for the predictions.