Snow cover is an important component of the global energy budget and the hydrologic cycle. However, retrieval of snow cover parameters from satellites has proven to be a daunting challenge, partly because of the extremely dynamic nature of snow and spatial heterogeneity. Passive microwave remote sensing technology provides a unique opportunity to address this challenge because of its all-weather capability and response to snow cover. Ultimate objective of our project is to develop a snow depth retrieval algorithm based on the Advanced Microwave Sounding Unit (AMSU). This project is part of the Microwave Surface Precipitation Product System (MSPPS) ongoing project at NOAA/NESDIS. MSPPS is designed to retrieve operational near real time surface and precipitation products from AMSU data. A snow cover extent algorithm has been incorporated into MSPPS and is operational. However, no snow depth retrieval algorithm from satellites has become fully operational. In this paper, we present our vision to addressing this challenge and discuss results of case studies. Ground based observations of snow cover for the November 2000-March 2001 period were obtained at selected stations in US from the Cooperative Weather Stations Network of the National Weather Service. Stations were selected such that areas they represent are relatively homogeneous, and coverage is dense enough. This dataset is further extended through computer model simulations to provide estimates of snow water equivalent, temperature and grain size. Snowpack undergoes continuous accumulation, compaction and metamorphosis, and melt. Use of the model as a means of providing estimates in concert with environmental observations serve the purpose of interpreting passive microwave signatures. Next, AMSU microwave signatures in the 23-150 GHz range at the selected station locations were retrieved through global output maps (HDF-EOS format) produced by NASA. These signatures are analyzed and compared with in-situ observations and model estimates of snow cover.