Earth as a Proxy Exoplanet: Deconstructing and Reconstructing Spectrophotometric Light Curves

TL;DR

This study uses Earth observations as a proxy to analyze spectral light curves, revealing how different surface and atmospheric features influence the light curve variability, aiding future exoplanet habitability assessments.

Contribution

It introduces a novel approach to decompose Earth’s spectral light curves into principal components and relate them to spatial features using machine learning, establishing a benchmark for exoplanet analysis.

Findings

First and fourth PCs relate to cloud types, explaining ~83% of variability.

Second PC contains surface reflectance contrast information.

Surface features like land and ocean are less distinguishable in light curves.

Abstract

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.