  Cloud formations often obscure optical satellite-based monitoring of the
Earth's surface, thus limiting Earth observation (EO) activities such as land
cover mapping, ocean color analysis, and cropland monitoring. The integration
of machine learning (ML) methods within the remote sensing domain has
significantly improved performance on a wide range of EO tasks, including cloud
detection and filtering, but there is still much room for improvement. A key
bottleneck is that ML methods typically depend on large amounts of annotated
data for training, which is often difficult to come by in EO contexts. This is
especially true when it comes to cloud optical thickness (COT) estimation. A
reliable estimation of COT enables more fine-grained and application-dependent
control compared to using pre-specified cloud categories, as is commonly done
in practice. To alleviate the COT data scarcity problem, in this work we
propose a novel synthetic dataset for COT estimation, that we subsequently
leverage for obtaining reliable and versatile cloud masks on real data. In our
dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral
bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms.
These data points have been simulated under consideration of different cloud
types, COTs, and ground surface and atmospheric profiles. Extensive
experimentation of training several ML models to predict COT from the measured
reflectivity of the spectral bands demonstrates the usefulness of our proposed
dataset. In particular, by thresholding COT estimates from our ML models, we
show on two satellite image datasets (one that is publicly available, and one
which we have collected and annotated) that reliable cloud masks can be
obtained. The synthetic data, the collected real dataset, code and models have
been made publicly available at
https://github.com/aleksispi/ml-cloud-opt-thick.
