The spectral and chemical measurement of pollutants on snow near South Pole, Antarctica (original) (raw)
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On the reflectance spectroscopy of snow
The Cryosphere Discussions, 2018
We propose a system of analytical equations to retrieve snow grain size and absorption coefficient of pollutants from snow reflectance or snow albedo measurements in the visible and near-infrared regions of the electromagnetic spectrum. It is assumed that ice grains and impurities (e.g., dust, black and brown carbon) are externally mixed. The system of nonlinear equations is solved analytically in the assumption that impurities influence registered spectra in the visible and not at near-infrared (and vice versa for ice grains). The theory is validated using spectral reflectance measurements and albedo of clean and polluted snow at various locations (Antarctica Dome C, European Alps). The technique to derive the snow albedo (plane and spherical) from reflectance measurements at a fixed observation geometry is proposed. The technique also enables the simulation of hyperspectral snow reflectance measurements in the broad spectral range from ultraviolet to the near-infrared for a given ...
Using a hemispherical directional reflectance factor instrument, spectral data of dirty snow containing black carbon (BC), mineral dust (MD), and ash was collected from multiple locations to investigate the impact of these lightabsorbing impurities (LAIs) on snow reflectance characteristics. The findings revealed that the perturbation of snow reflectance caused by LAIs is characterized by nonlinear deceleration, indicating that the reduction in snow reflectance per unit ppm of LAIs declines as snow contamination increases. The reduction in snow reflectance caused by BC may reach saturation at elevated particle concentrations (thousands of ppm) on snow. Snowpacks loaded with MD or ash initially exhibit a significant reduction in spectral slope around 600 and 700 nm. The deposition of numerous MD or ash particles can increase snow reflectance beyond the wavelength of 1400 nm, with an increase of 0.1 for MD and 0.2 for ash. BC can darken the entire measurement range (350−2500 nm), while MD and ash can only affect up to 1200 nm (350−1200 nm). This study enhances our understanding of the multi-angle reflection characteristics of various dirty snow, which can guide future snow albedo simulations and improve the accuracy of LAIs' remote sensing retrieval algorithms.
On the reflectance spectroscopy of snow 1
1. We acknowledge that the theory described must be extended to account for the possible snow vertical inhomogeneity and possible finite thickness of a snowpack. These topics are out of scope of this paper. The abstract, introduction, and conclusions have been modified to account for your comment. 2. We also agree that the retrieval approach will not work well in case of polluted snow with the spectral absorption coefficients of pollutants, which do not follow the Angström law. The abstract, introduction, and conclusions have been modified to account for your comment. Of course, general equations (Eqs. 1-3) to solve the direct problem of snow optics presented in the paper can be used anyway. Eq. 1 has a misprint (R0 is missed before the exponential term). We have corrected this misprint in the final version. 3. All equations and definitions are explained in the text. We have also prepared the Appendix A with all definitions and units. Also we have prepared a special section (appendi...
Interpretation of snow properties from imaging spectrometry
2009
Snow is among the most “colorful” materials in nature, but most of the variability in snow reflectance occurs beyond 0.8 µm rather than in the visible spectrum. In these wavelengths, reflectance decreases dramatically as the snow grains evolve and grow, whereas in the visible spectrum snow reflectance is degraded by contaminants such as dust, algae, and soot. From imaging spectrometer data, we can estimate the grain size of the snow in the surface layer, and thereby derive spectral and broadband albedo.