This investigation establishes a multisensor snow data assimilation system over North America (from January 2002 to June 2007), toward the goal of better estimation of snowpack (in particular, snow water equivalent and snow depth) via incorporating both Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) information into the Community Land Model. The different properties associated with the SCF and TWS observations are accommodated through a unified approach using the ensemble Kalman filter and smoother. Results show that this multisensor approach can provide significant improvements over a MODIS-only approach, for example, in the Saint Lawrence, Fraser, Mackenzie, Churchill & Nelson, and Yukon river basins and the southwestern rim of Hudson Bay. At middle latitudes, for example, the North Central and Missouri river basins, the inclusion of GRACE information preserves the advantages (compared with the open loop) shown in the MODIS-only run. However, in some high-latitude areas and given months the open loop run shows a comparable or even better performance, implying considerable room for refinements of the multisensor algorithm. In addition, ensemble-based metrics are calculated and interpreted domainwide. They indicate the potential importance of accurate representation of snow water equivalent autocovariance in assimilating TWS observations and the regional and/or seasonal dependence of GRACE’s capability to reduce ensemble variance. These analyses contribute to clarifying the effects of GRACE’s special features (e.g., a vertical integral of different land water changes, coarse spatial and temporal resolution) in the snow data assimilation system.