  Clouds and haze often occlude optical satellite images, hindering continuous,
dense monitoring of the Earth's surface. Although modern deep learning methods
can implicitly learn to ignore such occlusions, explicit cloud removal as
pre-processing enables manual interpretation and allows training models when
only few annotations are available. Cloud removal is challenging due to the
wide range of occlusion scenarios -- from scenes partially visible through
haze, to completely opaque cloud coverage. Furthermore, integrating
reconstructed images in downstream applications would greatly benefit from
trustworthy quality assessment. In this paper, we introduce UnCRtainTS, a
method for multi-temporal cloud removal combining a novel attention-based
architecture, and a formulation for multivariate uncertainty prediction. These
two components combined set a new state-of-the-art performance in terms of
image reconstruction on two public cloud removal datasets. Additionally, we
show how the well-calibrated predicted uncertainties enable a precise control
of the reconstruction quality.
