Characterization of artifacts introduced by the empirical volcano-scan atmospheric correction commonly applied to CRISM and OMEGA near-infrared spectra

The empirical 'volcano-scan' atmospheric correction is widely applied to martian near infrared CRISM and OMEGA spectra between similar to 4000 and similar to 2600 nm to remove prominent atmospheric gas absorptions with minimal computational investment. This correction method employs division by a scaled empirically-derived atmospheric transmission spectrum that is generated from observations of the martian surface in which different path lengths through the atmosphere were measured and transmission calculated using the Beer-Lambert Law. Identifying and characterizing both artifacts and residual atmospheric features left by the volcano-scan correction is important for robust interpretation of CRISM and OMEGA volcano-scan corrected spectra. In order to identify and determine the cause of spectral artifacts introduced by the volcano-scan correction, we simulated this correction using a multiple scattering radiative transfer algorithm (DISORT). Simulated transmission spectra that are similar to actual CRISM-and OMEGA-derived transmission spectra were generated from modeled Olympus Mons base and summit spectra. Results from the simulations were used to investigate the validity of assumptions inherent in the volcano-scan correction and to identify artifacts introduced by this method of atmospheric correction. We found that the most prominent artifact, a bowl-shaped feature centered near 2000 nm, is caused by the inaccurate assumption that absorption coefficients of CO2 in the martian atmosphere are independent of column density. In addition, spectral albedo and slope are modified by atmospheric aerosols. Residual atmospheric contributions that are caused by variable amounts of dust aerosols, ice aerosols, and water vapor are characterized by the analysis of CRISM volcano-scan corrected spectra from the same location acquired at different times under variable atmospheric conditions. (C) 2016 Published by Elsevier Inc.