  Cloud removal is a relevant topic in Remote Sensing as it fosters the
usability of high-resolution optical images for Earth monitoring and study.
Related techniques have been analyzed for years with a progressively clearer
view of the appropriate methods to adopt, from multi-spectral to inpainting
methods. Recent applications of deep generative models and
sequence-to-sequence-based models have proved their capability to advance the
field significantly. Nevertheless, there are still some gaps, mostly related to
the amount of cloud coverage, the density and thickness of clouds, and the
occurred temporal landscape changes. In this work, we fill some of these gaps
by introducing a novel multi-modal method that uses different sources of
information, both spatial and temporal, to restore the whole optical scene of
interest. The proposed method introduces an innovative deep model, using the
outcomes of both temporal-sequence blending and direct translation from
Synthetic Aperture Radar (SAR) to optical images to obtain a pixel-wise
restoration of the whole scene. The advantage of our approach is demonstrated
across a variety of atmospheric conditions tested on a dataset we have
generated and made available. Quantitative and qualitative results prove that
the proposed method obtains cloud-free images, preserving scene details without
resorting to a huge portion of a clean image and coping with landscape changes.
