Analog assimilation has recently been introduced as a data-driven alternative to model-driven data assimilation. The key idea is to build an exemplar-based dynamical model from a representative dataset of exemplars of the considered state-space. Here, we specifically address and discuss analog assimilation for high-dimensionsal state-space, more specifically spatio-temporal fields. We introduce a novel model, which combines a patch-based representation to a multi-scale and PCA-based decomposition. This model amounts to decomposing the global high-dimensional assimilation problem into a series of local low-dimensional assimilation problems. We demonstrate its relevance through an application to the reconstruction of spatio-temporal fields from irregularly-sampled observations. As case-study, we consider the spatio-temporal interpolation of satellite-derived sea surface geophysical fields. We further discuss large-scale implementation issues associated with the analog assimilation.