We present the first application of complex systems time series analysis to planetary data, with a long term objective of quantifying the observable complexity of exoplanets. The characterisation of exoplanets and the identification of extraterrestrial life are two of the most fundamental objectives of planetary science. We approach these questions by using Earth as a proxy exoplanet, since it is the only known inhabited world and because it is constantly observed in high resolution by various satellites. This study makes use of time series data from the Deep Space Climate Observatory (DSCOVR) satellite. We use reflectance images in 10 different wavelength channels from the ‘EPIC’ camera, that are coarse-grained to single pixels to create 10 time series. This situation emulates the long term, single-pixel observation of a distant exoplanet. We measure the ‘statistical complexity’ of these time series using computational mechanics techniques. To create additional test cases, the original images were modified to reduce the number of surface features, for example by replacing cloud pixels in the original images with pixel values more typical of land. The complexity analysis was re-applied to these synthetic time series to assess how the removal and replacement of planetary surface features reduces the observable complexity of the planet. We present these results and discuss future work that will extend our approach to other planetary bodies, to assess the hypothesis that the observable complexity of Earth is higher than other observed and simulated planets. If the hypothesis holds, we can then assess whether there is a causal link between Earth’s observable complexity and the presence of life.