Abstract ID: 3.12073 | Accepted as Talk | Talk/Oral | TBA | TBA

The cryosphere is a key indicator of climate variability and an essential component of the Earth’s climate system. Spectral remote sensing data enable the precise retrieval of different snow and ice properties, as well as the concentration of Light-Absorbing Particles (LAPs). LAPs, including organic impurities (e.g., cryospheric algae) and inorganic particles (e.g., mineral dust), are recognized as significant drivers of cryospheric change. These particles absorb solar radiation, darkening snow and ice surfaces and enhancing the snow-albedo feedback mechanism. This radiative forcing impacts the timing and volume of meltwater, with significant implications for ecosystems and human communities. Here, we present an analysis conducted in the European Alps (Plateau Rosa, Aosta Valley) by means of remote sensing, modelling and field campaigns, aiming at describing the LAP (Saharan dust) effect over seasonal snow cover. With the aim of spatialise the analyses in space and time, images acquired by Sentinel-2 and PlanetScope satellites were utilised. This approach allowed the monitoring of dust deposition and related radiative impact estimation on the snow cover of the study area, by applying different existing indices and parameters (Snow Darkening Index – SDI, Green-blue normalized index – GBNI, Impurity Index, albedo, NDSI). Moreover, we focused on the retrieval and validation of snow parameters using hyperspectral data from the PRISMA satellite, a radiative transfer modelling, and field campaigns (reflectance measurements and snow samples useful to measure density, liquid water content, grain size and dust concentration). As for the model, we advanced the HyS snow model from a melt-freeze temperature-index to an energy budget model. We evaluated LAP effects on snowpack dynamics, including snow depth, SWE, density and runoff, by capturing its impact on albedo and snow surface temperature as key factors influencing the energy fluxes of the snowpack specially during melting period. This research highlights the critical role of integrating remote sensing, field validation, and modelling to overcome challenges in snow parameter retrieval, to support climate research and promote sustainable water resource management. This work has been supported by the “Light-Absorbing ParticleS in the cryosphere and impact on water resourcEs (LAPSE)” project funded by MUR in the “PRIN22” program.