  Temporal sequences of satellite images constitute a highly valuable and
abundant resource for analyzing regions of interest. However, the automatic
acquisition of knowledge on a large scale is a challenging task due to
different factors such as the lack of precise labeled data, the definition and
variability of the terrain entities, or the inherent complexity of the images
and their fusion. In this context, we present a fully unsupervised and general
methodology to conduct spatio-temporal taxonomies of large regions from
sequences of satellite images. Our approach relies on a combination of deep
embeddings and time series clustering to capture the semantic properties of the
ground and its evolution over time, providing a comprehensive understanding of
the region of interest. The proposed method is enhanced by a novel procedure
specifically devised to refine the embedding and exploit the underlying
spatio-temporal patterns. We use this methodology to conduct an in-depth
analysis of a 220 km$^2$ region in northern Spain in different settings. The
results provide a broad and intuitive perspective of the land where large areas
are connected in a compact and well-structured manner, mainly based on
climatic, phytological, and hydrological factors.
