Lakes are of vital importance to the Chinese population for example by providing a source of drinking water. Taihu Lake is iconic in this perspective by being an important water source for its surrounding cities. The lake experiences frequent reoccurring algal blooms since 2007, threatening the drinking water safety of tens of millions of people. Therefore, it is valuable to study the spatial and temporal distribution of algal bloom and analyse which factors impact algal blooms in Taihu Lake to support the prediction of algal blooms. In traditional studies of algal blooms, there are limitations in data size and data analytical methods. This project will use big data and deep learning technologies to obtain more accurate algal distribution data in Taihu Lake, analyze its spatial and temporal distribution, identify key influencing factors, and apply this data and information into agal bloom data-driven models.