Radiometric investigation of different snow covers in Svalbard

This paper examines the relationship between reflectance and physical characteristics of the snow cover in the Arctic. Field data were acquired for different snow and ice surfaces during a survey carried out at Ny-Ålesund, Svalbard, in spring 1998. In each measurement reflectance in the spectral range 350 - 2500 nm, snow data (including temperature, grain size and shape, density and water content), surface layer morphology, and vertical profile of the snow pack were recorded detailed analysis of reflectance based on the physical was performed. Field reflectance data were also re-sampled at the spectral intervals of Landsat TM to compare the ability of identifying different snow targets at discrete wavelength intervals. This analysis shows that reliable data on snow structure and thickness are necessary to understand albedo changes of the snow surfaces

SNOW PRECIPITATION IN THE LAST YEARS ON ITALIAN ALPS

Recent papers showed a general decrease of winter precipitation in the last years on Italian Alps and particularly snowy precipitations, both in the Western Italian Alps and in the Eastern part. In the Dolomites snowfall decreased of 28% during the period 1988-2003, comparing with average precipitations in 30 years (1971-2000). In this paper it is carried out an analysis of seasonal snowfall in the Dolomites and first results of a regional climatic evolution using the adimensional index SAT (Standardized Anomaly Index) Tor 40 nivological stations on the Alps. Furthermore decreasing snow precipitations corresponds to a strong reduction of Dolomites\u27 glaciers (-24% of the surface during the period 1980-2000)

Study of the snow melt–freeze cycle using multi-sensor data and snow modelling

The melt cycle of snow is investigated by combining ground-based microwave radiometric measurements with conventional and meteorological data and by using a hydrological snow model. Measurements at 2000 m a.s.l in the basin of the Cordevole river in the eastern Italian Alps confirm the high sensitivity of microwave emission at 19 and 37 GHz to the snow melt–freeze cycle, while the brightness at 6.8 GHz is mostly related to underlying soil. Simulations of snowpack changes performed by means of hydrological and electromagnetic models, driven with meteorological and snow data, provide additional insight into these processes and contribute to the interpretation of the experimental data

COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

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

The Collection of Hyperspectral Measurements on Snow and Ice Covers in Polar Regions (SISpec 2.0)

The data value of hyperspectral measurements on ice and snow cover is strongly impacted by the availability of data services, where spectral libraries are integrated to detailed descriptions of the observed surface cover. For snow and ice cover, we present an updated version of the Snow/Ice Spectral Archive (SISpec 2.0), which has been integrated into a web portal characterized by different functionalities. The adopted metadata scheme features basic geographic data, information about the acquisition setup, and parameters describing the different surface types. While the implementation of the IACS Classification of Seasonal Snow on the Ground is the core component for snow cover, ice cover is approached using different parameters associated with its surface roughness and location. The web portal is not only a visualization tool, but also supports interoperability functionalities, providing data in the NetCDF file format. The availability of these functionalities sets the foundation for sharing a novel platform with the community and is an interesting tool for calibrating and validating data and models

The Collection of Hyperspectral Measurements on Snow and Ice Covers in Polar Regions (SISpec 2.0)

The data value of hyperspectral measurements on ice and snow cover is strongly impacted by the availability of data services, where spectral libraries are integrated to detailed descriptions of the observed surface cover. For snow and ice cover, we present an updated version of the Snow/Ice Spectral Archive (SISpec 2.0), which has been integrated into a web portal characterized by different functionalities. The adopted metadata scheme features basic geographic data, information about the acquisition setup, and parameters describing the different surface types. While the implementation of the IACS Classification of Seasonal Snow on the Ground is the core component for snow cover, ice cover is approached using different parameters associated with its surface roughness and location. The web portal is not only a visualization tool, but also supports interoperability functionalities, providing data in the NetCDF file format. The availability of these functionalities sets the foundation for sharing a novel platform with the community and is an interesting tool for calibrating and validating data and models

Automated Classification of Terrestrial Images: The Contribution to the Remote Sensing of Snow Cover

The relation between the fraction of snow cover and the spectral behavior of the surface is a critical issue that must be approached in order to retrieve the snow cover extent from remotely sensed data. Ground-based cameras are an important source of datasets for the preparation of long time series concerning the snow cover. This study investigates the support provided by terrestrial photography for the estimation of a site-specific threshold to discriminate the snow cover. The case study is located in the Italian Alps (Falcade, Italy). The images taken over a ten-year period were analyzed using an automated snow-not-snow detection algorithm based on Spectral Similarity. The performance of the Spectral Similarity approach was initially investigated comparing the results with different supervised methods on a training dataset, and subsequently through automated procedures on the entire dataset. Finally, the integration with satellite snow products explored the opportunity offered by terrestrial photography for calibrating and validating satellite-based data over a decade