The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankyla Finnish Lapland in 2009-2010

Seasonal snow surface roughness is an important parameter for remote sensing data analysis since it affects the scattering properties of the snow surface. To understand the phenomenon, snow surface roughness was measured near the town of Sodankyla in Finnish Lapland, during winters 2009 and 2010 using a photogrammetry-based plate method. The images were automatically processed so that an approximately 1 m long horizontal profile was extracted from each image. The data set consists of 669 plate profiles from different times and canopy types. This large data set was used to study the temporal and spatial variability of seasonal snow surface roughness. The profiles were analyzed using parameters derived from the root mean square height (sigma) and correlation length (L) as functions of measured length. Also, the autocorrelation functions were calculated and analyzed. The (sigma) and (L) were found to be so strongly correlated (R-2 similar to 0.97) that a more detailed analysis was made using only the scaling parameters derived from s. These parameters are related to the distance dependence of the rms height. The results show that they react to different characteristics of the profiles and are therefore well able to distinguish between different types of snow. They also show a clear difference between midwinter snow and melting snow, and the effects of snowfall events and slower melting in forested areas are evident in the data.