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Possible climate warming in northern latitudes will affect stream flow in watersheds dominated by snowmelt. It is important to detect early snowmelt conditions for applications to flood forecasting and monitoring fresh water stored in snow cover. This letter presents a study to demonstrate the potential of combined active and passive microwave spaceborne observations for monitoring global to regional snow cover on a daily basis. The proposed approach uses the temporal gradient of two parameters: 1) the Ku-band QuikSCAT scatterometer backscattering coefficient variation for snow wetness detection and 2) a dual-frequency emissivity index (Δϵt) derived from the Defense Meteorological Satellite Program Special Sensor Microwave Imager brightness temperatures for snow line detection. This approach takes into account the land cover derived from satellite data on a pixel-by-pixel basis. The evolution of the backscatter and Δϵt signatures throughout the winter-spring seasonal snow cycle is compared with in situ snow and air temperature measurements and with the snow-cover maps derived from high-resolution satellite data over Eastern Canada. The results show the high potential of this approach for historical analysis, as well as for day-to-day prospective investigation (forecasting).