The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled in time and space by various instruments and systems. Statistical models, especially spatio-temporal ones, are useful for enabling inferences, forecasts and decisions from sparse oceanic observations. This article focuses on the statistical treatment of two types of in situ observations in the Global Ocean Observing System: from profiling floats and surface drifters, focusing on the Argo and Global Drifter Programs. We describe the spatio-temporal models that have been developed in recent years for these data, to give a picture of the statistical challenges faced in modern physical oceanography. We will discuss in detail two types of reference frames when modeling such data: Eulerian and Lagrangian, the former of which is more appropriate for profiling floats and the latter for surface drifters.

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