Abstract
In this work, X band images acquired by COSMO-SkyMed (CSK) on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR) images (Landsat-8 and CSK) in separating snow/no-snow areas and in detecting wet snow. The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS) was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R2 = 0.77, and root mean square error, RMSE = 1.1 dB, with p-value <0.05.
Highlights
Snow, covering up to 50,000,000 km2 of the Earth surface, is an important component of the global water cycle, being a vital resource of fresh water and influencing atmospheric circulation and climate at both regional and global scales
The information required by the hydrological models for estimating the seasonal snow cover, concerns the snow spatial extent; the snowpack properties, such as grain size and albedo; the snow depth; and the snow water equivalent
A preliminary multi-temporal analysis was performed on CSK and Landsat-8 images, when simultaneous acquisitions are available, with the aim of confirming the results obtained at C band in previous works for the identification of different surface classes
Summary
Snow, covering up to 50,000,000 km of the Earth surface, is an important component of the global water cycle, being a vital resource of fresh water and influencing atmospheric circulation and climate at both regional and global scales. The information required by the hydrological models for estimating the seasonal snow cover, concerns the snow spatial extent; the snowpack properties, such as grain size and albedo; the snow depth; and the snow water equivalent. These parameters significantly influence the hydrological cycle and are key elements for the monitoring of cryosphere dynamics, since snow cover changes very rapidly in time (due to snow precipitation, freeze/thaw, snow accumulation) and in space (i.e., different deposition according to the surface type). The recent Earth observation missions as Sentinel-2 and Landsat-8, dedicated to the observation in the optical spectrum, Sensors 2017, 17, 84; doi:10.3390/s17010084 www.mdpi.com/journal/sensors
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