In multi-satellite formations for Earth observation, downlink capacity is a key bottleneck in data acquisition. As measurement data from different satellites of the same mission is often correlated to some degree, modern distributed source coding techniques are a promising approach for increasing downlink coding efficiency, thereby improving the overall mission data acquisition capability. Distributed arithmetic coding (DAC) has been extensively studied but has been applied primarily either on synthetic multi-source pseudo-random data or in single-source scenarios with real measurement data. In order to apply DAC to independent sources' measurement data of unknown entropy and correlation, we propose an adaptive DAC decoder and different use-case specific fitness functions. Using real world data from the MagSat mission, we demonstrate that correlations in physical measurement data can be exploited to increase coding efficiency of downlinks in multi-satellite Earth observation missions.
