  Satellite imaging has a central role in monitoring, detecting and estimating
the intensity of key natural phenomena. One important feature of satellite
images is the trade-off between spatial/spectral resolution and their
revisiting time, a consequence of design and physical constraints imposed by
satellite orbit among other technical limitations. In this paper, we focus on
fusing multi-temporal, multi-spectral images where data acquired from different
instruments with different spatial resolutions is used. We leverage the spatial
relationship between images at multiple modalities to generate high-resolution
image sequences at higher revisiting rates. To achieve this goal, we formulate
the fusion method as a recursive state estimation problem and study its
performance in filtering and smoothing contexts. The proposed strategy clearly
outperforms competing methodologies, which is shown in the paper for real data
acquired by the Landsat and MODIS instruments.
