Abstract

We use multispectral MODIS/ASTER Airborne Simulator (MASTER) data collected at Mt. Rainier, Washington (USA) to map spatial covariance between snowpack properties and to evaluate techniques for quantitative estimation of reflectance, grain size, and temperature. The late-August MASTER images reveal a distinct pattern of snow contaminant content, grain size, and temperature related to a recent snowfall and late-summer melting. Spatial correlation between grain size and temperature patterns suggests that rapid destructive metamorphism of the fresh snow occurred when temperatures were near 0 °C. We use 10 specific locations to evaluate hemispherical–directional reflectance factor (HDRF), grain size, and temperature retrievals. We map relative snow contaminant content using visible (0.4–0.8 μm) HDRF spectra. Atmospheric correction and topographic modeling limit the accuracy of HDRF estimates. We use MASTER-derived spectra near 1.8 and 2.2 μm to estimate optical grain size (by comparison to modeled layers of ice spheres) and physical grain size (by comparison to measured spectra with known physical grain size and by correlation to ground measurements). Estimated physical grain sizes were less than estimated optical grain sizes. Differing definitions of optical and physical grain sizes could contribute to this discrepancy. Limitations at 1.8 and 2.2 μm, including reduced discrimination between larger grain radii (>∼500 μm physical, >∼200 μm optical) and low signal-to-noise ration with atmospheric effects and decreasing solar irradiance, suggest that grain size retrieval may be improved at other wavelengths (e.g., 1.1 μm). Accounting for uncertainty in emissivity, atmospheric correction, and detector noise, we estimate systematic errors in our radiant temperatures at <1.8 °C. This study shows both strengths and limitations for coregistered visible, short-wave infrared, and thermal infrared images to estimate snowpack properties and reveal their spatial coherence.

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