Our understanding of the environment is greatly associated with the interlinked knowledge of the phenomena surrounding to us. Such knowledge is a result of data and extracted information. With the availability of very high and even ultra-high resolution sensor data there is a greater need of managing data, information and essentially the knowledge. With the advent of technological novelties and their wider applications the generated data is surpassing our capacities to store it. There is an urgent need for improved methods and advancement in data-intensive science to retrieve, filter, integrate, and share data. Data and meaningful information are key for the actors in every walk of life, however, how to conceive, perceive, recognize and interpret such data in space and time is a big question and a big challenge. Taking this challenge into the perspective, we have presented an opportunity of recommending environmental big data using machine learning approaches. We have a firm belief that our simple approach will contribute to the body of knowledge in big data study and big knowledge management in this era of data intensive science.