We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixelweighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode.
[1] The distribution of snow and the energy flux components of snowmelt are intrinsic characteristics of the alpine water cycle controlling the location of source waters and the effect of climate on streamflow. Interannual variability of these characteristics is relevant to the effect of climate change on alpine hydrology. Our objective is to characterize the interannual variability in the spatial distribution of snow and energy fluxes of snowmelt in watersheds of a maritime setting, Tokopah Basin (TOK) in California's southern Sierra Nevada, and a continental setting, Green Lake 4 Valley (GLV4) in Colorado's Front Range, using a 12 year database (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) of hydrometeorological observations and satellite-derived snow cover. Snowpacks observed in GLV4 exhibit substantially greater spatial variability than in TOK (0.75 versus 0.28 spatial coefficient of variation). In addition, modeling results indicate that the net turbulent energy flux contribution to snowmelt in GLV4 is, on average, 3 times greater in magnitude (mean 29% versus 10%) and interannual variability (standard deviation 17% versus 6%) than in TOK. These energy flux values exhibit strong seasonality, increasing as the melt season progresses to times later in the year (R 2 ¼ 0.54-0.77). This seasonality of energy flux appears to be associated with snowmelt rates that generally increase with onset date of melt (0.02 cm d
À2). This seasonality in snowmelt rate, coupled to differences in hydrogeology, may account for the observed differences in correspondence between the timing of snowmelt and timing of streamflow in these watersheds.
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