### Limitations of Remote Sensing Snow Techniques  
The complexity of snow further confounds the retrieval of snow properties using remotely-sensed observations such that no single technique can work for all types of snow. One significant source of uncertainty in retrieving snow mass comes from the sensitivity of remotely-sensed observations to other snow-related variables, such as snow microstructure (e.g., grain size), snow stratigraphy, the amount of liquid water content coating the snow grains, overlying vegetation, complex topography, and atmospheric and cloud conditions [17]. Microwave radiation, in general, is sensitive to the snow liquid water content (SLWC) such that even a small amount of liquid water in the snowpack greatly alters the dielectric constant and, hence, the emissivity and absorptivity of the snowpack. SLWC, which is relatively large during the melting season, often introduces large uncertainties into SWE retrievals from both PMW and AMW retrievals [31]. Snow density, snow grain size, and snow grain shape are other variables that influence the snow emissivity and scattering characteristics and, as a result, the corresponding electromagnetic response of the snowpack [12]. The variability in these snow characteristics can result in a strong correlation between the PMW signals and snow mass for some years, but not for other years. Additionally, the complex microstructure of a snowpack due to variations in depth honor, internal ice layering, and vertical heterogeneity increase the spatial and temporal variability of the snowpack [12]. Overlying vegetation further complicates snow remote sensing by attenuating microwave emission from the snowpack, while simultaneously contributing its own signal as measured by the spaceborne radiometer. Findings have shown that PMW SWE retrievals tend to underestimate SWE in forested areas [32]. Complex terrain, such as in mountains, also reduces the efficacy of coarse-resolution sensors, such as PMW radiometers or AMW scatterometers [17; 33; 34]. All of these uncertainties are exacerbated by the coarse-scale resolution of these measurements that cannot adequately capture the true spatial variability of SWE [35; 36]. To overcome this problem, recent studies improved large-scale patterns of snow mass estimation by merging PMW observations with ground-based measurements [19; 28].  
In the context of spaceborne LiDAR, which is another option for retrieving snow depth and snow mass, a major limitation is the relatively narrow swath width of LiDAR [37] (\(\sim\)10 km) as compared to SAR (\(\sim\)100 km) or PMW radiometry (\(\sim\)1000 km). The individual LiDAR beams typically obtain tracks with widths of 100 meters or less. Using multiple LiDAR beams as part of a sampling strategy, the swath width of a spaceborne LiDAR retrieval is typically around 6 km [38; 39]. Furthermore, the optical signal used by a snow LiDAR cannot penetrate optically-thick clouds [40]; hence, the snow under the clouds remains unobserved. LiDAR is an effective tool for retrieving snow depth, but that effectiveness is severely curtailed when considering swath width limitations and cloud attenuation.  
In short, no single spaceborne sensor will adequately measure all types of snow under all conditions required for global snow monitoring. Rather, a mixture of observations from different sensors, each with its own strengths and weaknesses, is needed to yield the best estimate of global snow mass [37].