New satellite constellations allow the acquisition of high temporal and spatial resolution images at any point on the Earth. These data, assembled in the form of satellite image time series (SITS), are an important source of information for monitoring the evolution of the Earth’s surface. Deep learning is one of the most promising solutions for the automatic analysis of large volumes of data acquired by new generations of satellites. However, these techniques often only exploit temporal or spatial structures. To take advantage of the temporal and spatial complementarity of the data without computational burden, we use graph-based modeling in combination with deep learning. In particular, we propose a comparison of five graph neural networks applied to SITS. The results highlight the efficiency of graph models in understanding the spatio-temporal context of regions, which might lead to a better classification compared to attribute-based methods.