Understanding and quantifying satellite-based remotely sensed snow cover errors are critical for successful utilization of snow cover products. The Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Covered Area (SCA) product errors have been previously recognized to be associated with factors such as cloud contamination, snow pack particles, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of MODIS SCA and land surface temperature (LST) products, and in-situ air temperature and snow water equivalent (SWE) measurements provides a unique look at the error structure of the recently developed MODIS SCA products. Analysis of the MODIS SCA data set over the period from 2000 to 2005 was undertaken for the California/Nevada and Colorado regions of the western United States. Both regions have extensive observational networks. For this study area, the MODIS SCA product demonstrates strong ability in detecting the presence of snow cover (80%). However, significant spatial and temporal variations in accuracy (from 75% in high roughness to 86% in low roughness regions and 45% in October to 94% in February) suggest that a proxy is required to adequately predict the MODIS SCA product errors. For the first time, we demonstrate a relationship between the errors in the MODIS SCA products and temperature in the western United States, and find that this relationship is well-represented by the cumulative double exponential distribution function. We have performed a fitting and validation experiment by deriving the relationship between temperature and the errors in the MODIS SCA product from 2000-2004 period and using 2005 to independently test our method. This relationship is shown to hold for both in-situ daily mean air temperature and MODIS LST. Both of them are useful indices in quantifying the errors in MODIS product for various hydrological applications.