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John Pomeroy, Chad Ellis, Aled Rowlands, Richard Essery, Janet Hardy, Tim Link, Danny Marks, and Jean Emmanuel Sicart

.63–0.85 for the uniform canopy on the overcast day. An analysis of variance of the regressions showed F statistics for all linear fits that were statistically significant, with a probability of 0.95 or better. The fit of the relationships generally improved with longer sampling intervals. The spatial CV for daily irradiance was calculated directly (the daily standard deviation divided by the daily mean). Figure 4 shows the change in the spatial CV with temporal sampling interval. Whereas meaningful tests

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Jeffrey S. Deems, Steven R. Fassnacht, and Kelly J. Elder

surface elevations. A 1-m resolution digital elevation model (DEM) was produced from the bare earth point data using inverse–distance–weighting interpolation. The DEM elevations were then subtracted from the snow surface elevation points, producing datasets of snow depth point estimates. The point datasets of snow depth, bare earth elevations, and vegetation topography were used for the variogram fractal analysis. Figure 6 shows the histograms for the derived snow depth point datasets. The deep snow

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Rafał Wójcik, Konstantinos Andreadis, Marco Tedesco, Eric Wood, Tara Troy, and Dennis Lettenmeier

and infrared radiances and brightness temperatures. CRTM is generally much more sophisticated in its representation of atmospheric radiative transfer than of the land surface. The land surface emissivity in CRTM is based on snow depth and surface temperature, from which they use an empirical regression for grain size as inputs into the land emission model ( Weng et al. 2001 ). Whether or not snow is present is determined by the output of the (Noah) land scheme snow depth prediction, which is

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Jicheng Liu, Curtis E. Woodcock, Rae A. Melloh, Robert E. Davis, Ceretha McKenzie, and Thomas H. Painter

fractional snow cover from remote sensing in forested areas. Remote sensing of snow cover has evolved from binary mapping of snow ( Dozier 1989 ; Hall et al. 2002 ) to subpixel mapping of fractional snow cover with regression tree approaches ( Rosenthal and Dozier 1996 ) with use of the normalized difference snow index (NDSI) ( Salomonson and Appel 2004 , 2006 ) and multiple endmember direct spectral mixture analysis ( Painter et al. 2003 ). Figure 2 shows the fractional snow-covered area (SCA

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Richard Essery, Peter Bunting, Aled Rowlands, Nick Rutter, Janet Hardy, Rae Melloh, Tim Link, Danny Marks, and John Pomeroy

while providing good definition of the forest boundary in denser areas. Four main techniques have been developed for automatic crown delineation in images: valley following ( Gougeon 1995 ), pattern matching ( Pollock 1996 ), crown centroid identification ( Culvenor 2002 ), and wavelet analysis ( Strand et al. 2006 ). The crown centroid method used here is described by Bunting and Lucas (2006) . Local maxima in NDVI are identified as crown centers and expanded to minima to form crown edges. A

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Susan Frankenstein, Anne Sawyer, and Julie Koeberle

on an approximately monthly basis were combined into one continuous file for each tower and filtered twice for faulty values then averaged to produce hourly time steps. The Fool Creek meteorological data represent conditions under the canopy. We estimated single missing data points from the averaged dataset by calculating the arithmetic mean of the previous and subsequent points. We used a linear regression equation calculated from the previous points and the three subsequent data points to

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