Snow pack properties such as snow depth and snow water equivalent (SWE) have always attracted hydro-meteorologists. The satellite borne microwave imagery has provided the option to produce maps of SWE spatial distribution. This research focuses on application of passive microwave data to estimate SWE developing an algorithm to estimate snowpack properties in Great Lakes area based on a three-year of SSM/I dataset along with corresponding ground truth data. The study area is located between latitudes 41N-49N and longitudes 87W-98W. The area is covered by 28*35 SSM/I EASE-Grid pixels with spatial resolution of 25km. Nineteen test sites were selected based on seasonal average snow depth, land cover type. Each of the sites covers an area of 25km*25km with minimum of one snow reporting station inside. Two types of ground truth data were used: 1) point-based snow depth observations from NCDC; 2) grid based SNODAS-SWE dataset, produced by NOHRSC. To account for land cover variation in a quantitative way a NDVI was used. To do the analysis, three scattering signatures of GTVN (19V-37V), GTH (19H-37H), and SSI (22V-85V) were derived. The analysis shows that at lower latitudes of the study area there is no correlation between GTH and GTVN versus snow depth. On the other hand SSI shows an average correlation of 75 percent with snow depth in lower latitudes which makes it suitable for shallow snow identification. In the model development a non-linear algorithm was defined to estimate SWE using SSM/I signatures along with the NDVI values of the pixels. The results show up to 60 percent correlation between the estimated SWE and ground truth SWE. The results showed that the new algorithm improved the SWE estimation by more than 20 percent for specific test sites.