Preliminary Assessment of the Impact of Lakes on Passive Microwave Snow Retrieval Algorithms in the Arctic

The retrieval of snow water equivalent (SWE) and snow depth (SD) information from passive microwave brightness temperatures is theoretically straightforward: as the depth and/or density of snow increases, so too does the amount of volume scatter of naturally emitted microwave energy. Shorter wavelength energy (i.e. 37 GHz) is more readily scattered than longer wavelength energy (i.e. 19 GHz), so the difference in scatter between these two frequencies (19 GHz–37 GHz) has been exploited to estimate SWE and SD. In reality, the relationship between snow depth, density, and microwave scatter is complicated by the physical structure of the snowpack (for example, ice lenses, the presence of liquid water, snow grain size variability) and the microwave emission and scattering characteristics of vegetation. The imaging footprint for spaceborne passive microwave data is large so these complicating factors are compounded by considerable within-grid cell variability in snowpack structure and any overlying vegetative cover. Snow surveys conducted during late winter of 2003 and 2004 in the Coppermine River basin of the Northwest Territories indicate that SSM/I derived estimates of SWE significantly underestimate actual SWE when utilizing an algorithm developed for SWE retrievals in open prairie environments, which we will refer to as the Goodison algorithm hereafter (Derksen an