  Small satellites are widely used today as cost effective means to perform
Earth observation and other tasks that generate large amounts of
high-dimensional data, such as multi-spectral imagery. These satellites
typically operate in low earth orbit, which poses significant challenges for
data transmission due to short contact times with ground stations, low
bandwidth, and high packet loss probabilities. In this paper, we introduce
JSCC-Sat, which applies joint source-and-channel coding using neural networks
to provide efficient and robust transmission of compressed image data for
satellite applications. We evaluate our mechanism against traditional
transmission schemes with separate source and channel coding and demonstrate
that it outperforms the existing approaches when applied to Earth observation
data of the Sentinel-2 mission.
