Determining Reflectance Spectra of Surfaces and Clouds on Exoplanets

TL;DR

This paper demonstrates a method to extract and map surface and cloud spectra of exoplanets from disk-integrated photometry data, enabling characterization of unknown planetary surfaces without prior assumptions.

Contribution

It introduces a novel rotational unmixing technique that simultaneously determines surface spectra and their geographical distribution from photometric data.

Findings

Successfully extracted Earth's surface and cloud spectra from EPOXI data.

The method can identify and map unknown surfaces on exoplanets.

Surface spectra resemble clouds, ocean, and land through a Rayleigh atmosphere.

Abstract

Planned missions will spatially resolve temperate terrestrial planets from their host star. Although reflected light from such a planet encodes information about its surface, it has not been shown how to establish surface characteristics of a planet without assuming known surfaces to begin with. We present a re-analysis of disk-integrated, time-resolved, multiband photometry of Earth obtained by the Deep Impact spacecraft as part of the EPOXI Mission of Opportunity. We extract reflectance spectra of clouds, ocean and land without a priori knowledge of the numbers or colors of these surfaces. We show that the inverse problem of extracting surface spectra from such data is a novel and extreme instance of spectral unmixing, a well-studied problem in remote sensing. Principal component analysis is used to determine an appropriate number of model surfaces with which to interpret the data. Shrink-wrapping a simplex to the color excursions of the planet yields a conservative estimate of the planet's endmember spectra. The resulting surface maps are unphysical, however, requiring negative or larger-than-unity surface coverage at certain locations. Our "rotational unmixing" supersedes the endmember analysis by simultaneously solving for the surface spectra and their geographical distributions on the planet, under the assumption of diffuse reflection and known viewing geometry. We use a Markov Chain Monte Carlo to determine best-fit parameters and their uncertainties. The resulting albedo spectra are similar to clouds, ocean and land seen through a Rayleigh-scattering atmosphere. This study suggests that future direct-imaging efforts could identify and map unknown surfaces and clouds on exoplanets.