Ocean data exhibits interesting yet human critical features affecting all creatures around the world. Studies on Hydrology and Oceanology become the root of many disciplines, including global resource management, macro economy, environment protection, climate predictions, etc, which motivates our further exploration on the underlying feature behind the ocean data. However, with high dimensionality, large quantities, heterogeneous sources, and especially, the spatiotemporal manner, the diversity between the specific knowledge required and massive data chunk puts forward unique challenges in data representation and knowledge mining, effectively. This paper tends to provide a summary of studies on these issues, including the data representation, data processing, knowledge discovery, and algorithms on finding unique patterns on ocean environment changes, such as temperature, tide height, waves, salinity, etc. In detail, we comprehensively discuss about ocean spatiotemporal data processing techniques. We further summarize related representation works on ocean spatiotemporal data, the construction of a ocean knowledge graph, and the management of ocean spatiotemporal data. At last, we combine and compare the collection of the evolution and multiple state-of-the-arts on ocean spatiotemporal data processing.