  Point source spectrophotometric ("single-point") light curves of Earth-like
planets contain a surprising amount of information about the spatial features
of those worlds. Spatially resolving these light curves is important for
assessing time-varying surface features and the existence of an atmosphere,
which in turn is critical to life on Earth and significant for determining
habitability on exoplanets. Given that Earth is the only celestial body
confirmed to harbor life, treating it as a proxy exoplanet by analyzing
time-resolved spectral images provides a benchmark in the search for habitable
exoplanets. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space
Climate Observatory (DSCOVR) provides such an opportunity, with observations of
~5000 full-disk sunlit Earth images each year at ten wavelengths with high
temporal frequency. We disk-integrate these spectral images to create
single-point light curves and decompose them into principal components (PCs).
Using machine learning techniques to relate the PCs to six preselected spatial
features, we find that the first and fourth PCs of the single-point light
curves, contributing ~83.23% of the light curve variability, contain
information about low and high clouds, respectively. Surface information
relevant to the contrast between land and ocean reflectance is contained in the
second PC, while individual land sub-types are not easily distinguishable
(<0.1% total light curve variation). We build an Earth model by systematically
altering the spatial features to derive causal relationships to the PCs. This
model can serve as a baseline for analyzing Earth-like exoplanets and guide
wavelength selection and sampling strategies for future observations.
