1. INTRODUCTION
Increased melting of snow and ice surfaces over the past decades significantly contributes to global sea level rise. Melting dynamics are controlled by the amount of solar radiation absorbed by the surface. This absorption increases when snow and ice are darkened, typically caused by accumulation of small light-absorbing particles (LAP). Amongst others, LAP include atmospheric mineral dust that originates from arid source regions. Detection and quantification of LAP is essential for predicting snow and ice melt. A new generation of orbital VSWIR imaging spectrometers provides the prerequisites needed to achieve this objective by featuring narrow spectral channels that are able to resolve subtle LAP absorption features. NASA’s Earth Surface Mineral Dust Source Investigation (EMIT), launched in July 2022 and installed on the International Space Station (ISS), aims to improve our understanding of the Earth’s mineral dust sources and their climate impacts. These impacts include dust deposition on snow and ice surfaces in mountainous regions. Recent work has demonstrated that a simultaneous inversion of atmosphere and surface state using optimal estimation (OE) shows promising potential to map snow geobiophysical quantities from spaceborne imaging spectroscopy observations [1], [2]. However, the current approach neglects the effects of topography, which could lead to significant biases in estimated LAP concentration, and propagate through to erroneous calculations of LAP radiative forcing. We present a modification of the algorithm by using a discrete anisotropic surface-atmosphere radiative transfer model that couples the MODTRAN code with a combination of Mie scattering calculations and the multistream DISORT program. We present estimated snow reflectance, dust concentration, and LAP radiative forcing from a selected EMIT scene acquired over the Patagonian Ice Sheet in South America and highlight retrieval sensitivity to topographic characteristics. Finally, we intend to provide a blueprint for the conception of future topography-aware retrieval algorithms that are essential for upcoming global spaceborne imaging spectroscopy missions, including NASA’s Surface Biology and Geology (SBG).