The pitfalls of ignoring topography in snow retrievals: a case study with EMIT

Abstract. Snow and ice surfaces are an important modulator of Earth’s climate as they reflect most of the incoming solar radiation favoring substantial cooling effects. Thereby, the amount of absorbed solar illumination regulates radiative forcing, which in turn steers melting processes on ice sheets and glaciers. Global patterns of snow darkening, induced by the accumulation of small light-absorbing particles (LAPs), such as dust or algae, lead to an intensified radiative forcing and melting of Earth’s snow cover. It is one of the driving factors for both global sea level rise and increasing air temperature. Mapping and quantifying LAPs on both temporal and spatial scales is therefore needed to improve the prediction of melt rates and their impacts on climate change. High-resolution visible-to-shortwave-infrared (VSWIR) imaging spectrometers herald a new era of passive spaceborne remote sensing, which will help to fulfill this objective. This technology provides continuous spectral channels throughout the solar spectrum, allowing to detect narrow LAP absorption bands. One of these instruments is NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) that was launched to the International Space Station (ISS) in July 2022. The prime mission focus is to deliver maps of surface minerals from arid dust source regions in order to inform Earth system models (ESM) of atmospheric transport and radiative forcing. In addition, the EMIT target mask also includes snow cover in low to mid-latitude mountainous regions, such as the Western US, the Andes in South America, or high-mountain Asia, all of which are prone to surface deposition of small dust particles after traveling hundreds to thousands of kilometers through the atmosphere. Accurate retrievals of snow surface properties in those regions require precise accounting for anisotropy and topography due to varying forward scattering intensity as a function of illumination and observation geometry. In this contribution, we invert a coupled surface-atmosphere radiative transfer model that joins the MODTRAN code with a combination of Mie scattering calculations and the multistream DISORT program. The model provides a physics-based parameterization of the surface, including illumination and observation angles, and facilitates a well-posed retrieval problem by significantly reducing the number of state vector elements. We apply the approach to EMIT images from Patagonia, South America, and present an analysis of retrieval sensitivity to local illumination conditions. We find uncertainties in snow grain size of up to 200 μm and in dust mass mixing ratio of up to 75 μg / g_snow when the model neglects the influence of topography. Furthermore, we demonstrate differences in LAP radiative forcing of up to 400 W / m² in cases of inaccurately quantified LAP concentration. Finally, we evidence that erroneous assumptions about surface topography are one of the major causes for the formation of the “blue hook" in remotely sensed retrievals of snow reflectance. These findings will be essential for updating melt runoff and climate model input, but also for the conception of retrieval algorithms for future orbital imaging spectroscopy missions, such as NASA’s Surface Biology and Geology (SBG).