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

Abstract

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.

Environmentally Induced Snow Transmittance Variations in the Photosynthetic Spectral Domain: Photobiological Implications for Subnivean Vegetation under Climate Warming Conditions

“…For consistency with the SISpec measured radiometric datasets [36] employed as baseline references in this investigation, the modelled radiometric curves were obtained considering an angle of incidence of 0 • (with respect to the samples' normal vector). We note that the SISpec measured radiometric datasets [36] correspond to reflectance factors calculated considering a hemispherical incidence geometry, with the a small acquisition sensor (optic fibre) positioned directly above (50 cm) the target surfaces. The modelled datasets, on the other hand, correspond to directional-hemispherical reflectances.…”

“…Initially, in Phase I, we computed reflectance curves for the selected snow samples. We then compared the modelled curves with their measured counterparts provided by Salvatori et al [36]. This enabled us to verify the plausibility of the snow characterization datasets (Table 1) and to establish reliable baselines for our transmittance experiments.…”

“…In order to meet the above guidelines, we searched for measured radiometric data accompanied by detailed descriptions of the target snowpacks. Accordingly, without loss of generality, we chose to use as baseline references for our investigation measured reflectance datasets obtained by Salvatori et al [36] on the Svalbard island, in the Arctic. More specifically, these datasets were made available through the Snow and Ice Spectral Library (SISpec 2.0) as spectral files #6 and #195.…”

“…Such a control is difficult to be achieved under field conditions in which the changes are elicited by environmental factors that may affect multiple nivological characteristics of the snowpack simultaneously. While such controlled experiments can be attempted under laboratory conditions, usually artificially prepared snow samples lack the morphological diversity (e.g., different grain sizes and shapes) found in natural samples [36], which limits the scope of the observations that can be derived from their outcomes. Moreover, experiments involving light transmission on scattering materials, conducted under field or laboratory conditions, have to face another challenging aspect, namely the control of incident illumination [1].…”

“…The employed in silico investigation approach is grounded on measured radiometric and nivological data obtained for real snowpacks located in the Arctic [36]. We note that this is the region most affected by climate change effects, with a warming rate more accentuated than the global warming rate [12,39].…”