  Earth observation with small satellites serves a wide range of relevant
applications. However, significant advances in sensor technology (e.g., higher
resolution, multiple spectrums beyond visible light) in combination with
challenging channel characteristics lead to a communication bottleneck when
transmitting the collected data to Earth. Recently, joint source coding,
channel coding, and modulation based on neuronal networks has been proposed to
combine image compression and communication. Though this approach achieves
promising results when applied to standard terrestrial channel models, it
remains an open question whether it is suitable for the more complicated and
quickly varying satellite communication channel. In this paper, we consider a
detailed satellite channel model accounting for different shadowing conditions
and train an encoder-decoder architecture with realistic Sentinel-2 satellite
imagery. In addition, to reduce the overhead associated with applying multiple
neural networks for various channel states, we leverage attention modules and
train a single adaptable neural network that covers a wide range of different
channel conditions. Our evaluation results show that the proposed approach
achieves similar performance when compared to less space-efficient schemes that
utilize separate neuronal networks for differing channel conditions.
