###### Abstract  
Terrestrial snow is a vital freshwater resource for more than 1 billion people. Remotely-sensed snow observations can be used to retrieve snow mass or integrated into a snow model estimate; however, optimally leveraging remote sensing observations of snow is challenging. One reason is that no single sensor can accurately measure all types of snow because each type of sensor has its own unique limitations. Another reason is that remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations may not meet the requirements of a given snow applications. In order to quantify the feasible availability of remotely-sensed observations across space and time, this study simulates the sensor coverage for a suite of hypothetical snow sensors as a function of different orbital configurations and sensor properties. The information gleaned from this analysis coupled with a dynamic snow binary map is used to evaluate the efficiency of a single sensor (or constellation) to observe terrestrial snow on a global scale. The results show the efficacy achievable by different sensors over different snow types. The combination of different orbital and sensor configurations is explored to requirements of remote sensing missions that have 1-day, 3-day, or 30-day repeat intervals. The simulation results suggest that 1100 km, 550 km, and 200 km are the minimum required swath width for a polar-orbiting sensor to meet snow-related applications demanding a 1-day, 3-day, and 30-day repeat cycles, respectively. The results of this paper provide valuable input for the planning of a future global snow mission. os 20221463  
the difficulties in collecting in-situ measurements over such large regions. Despite the extensive efforts of researchers to provide accurate SWE retrievals, current SWE products using passive microwave (PMW) remote sensing observations still do not meet the accuracy requirements (\(\pm\)15%) needed to support operational decision-making at a continental or global scale [13; 14]. One main reason is the knowledge gap in coupling precise physical emission models (i.e., radiative transfer models (RTMs)) of snowpack to remotely-sensed observations [12]. A general approach for using PMW observations is the spectral polarization difference (SPD) [15] employing the Chang algorithm [16] and its modification for forested areas ([PERSON] 1997); this approach uses PMW spectral difference, i.e., the difference in brightness temperature between two microwave frequency channels. This technique is effective in some regions e.g., dry, shallow snow on flat terrain, but is unable to detect thin snow due to a low signal-to-noise ratio, or accurately retrieve deep snow due to signal saturation, or snow with overlying vegetation due to vegetation attenuation [12; 17]. To help overcome the problems of PMW observations across regional or continental scales, some studies have fused satellite data with ground-based snow measurements to better estimate SWE [18; 19]. There has never been a dedicated satellite mission for snow mass (SWE) detection. Existing spaceborne sensors used to estimate snow mass have typically been designed for a different purpose [20]. With the above limitations, most operational, stand-alone passive microwave SWE products produced using the spectral difference method are far from optimal. Typically, these products are inconsistent with independent reanalysis data and ground-based measurements from meteorological stations and snow courses [10; 21], particularly in deep snow, wet snow, snow in complex terrain, or snow with overlying vegetation [10; 17; 22]. Analogously, the Moderate Resolution Imaging Spectroradiometer (MODIS), a passive visible and thermal infrared radiometer, was designed to view the spatial extent of snow rather than the mass of snow within that snow-covered extent.