Terrestrial freshwater storage (TWS) is the vertically-integrated sum of snow, ice, soilmoisture, vegetation water content, surface water impoundments, and groundwater. Among these components, snow, soil moisture, and vegetation are the most dynamic (i.e., shortest residence time) as well as the most variable across space. However, accurately retrieving estimates of snow, soil moisture, or vegetation using space-borne sensors often requires simultaneous knowledge of one or more of the other components. In other words, reasonably characterizing terrestrial freshwater requires careful consideration of the coupled snow-soil moisture-vegetation response that is implicit in both TWS and the hydrologic cycle. One challenge is to optimally determine the multi-variate, multi-sensor remote sensing observations needed to best characterize the coupled snow-soil moisture-vegetation system. Different types of sensors each have their own unique strengths and limitations. Meanwhile, remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations will dictate much of the efficacy in capturing the dynamics of the coupled snow-soil moisture-vegetation response. This study investigates different snow sensors and simulates the sensor coverage as a function of different orbital configurations and sensor properties in order to quantify the discontinuous nature of remotely-sensed observations across space and time. The information gleaned from this analysis, coupled with a time-varying snow binary map, is used to evaluate the efficacy of a single sensor (or constellation of sensors) to estimate terrestrial snow on a global scale. A suite of different combinations, and permutations, of different sensors, including different orbital characteristics, is explored with respect to 1-day, 3-day, and 30-day repeat intervals. The results show what can, and what cannot, be observed by different sensors. The results suggest that no single sensor can accurately measure all types of snow, but that a constellation composed of different types of sensors could better compensate for the limitations of a single type of sensor. Even though only snow is studied here, a similar procedure could be conducted for soil moisture or vegetation. To better investigate the coupled snow-soil moisture-vegetation system, an observing system simulation experiment (OSSE) is designed in order to explore the value of coordinated observations of these three separate, yet mutually dependent, state variables. In the experiment, a “synthetic truth” of snow water equivalent, surface soil moisture, and/or vegetation biomass is generated using the NoahMP-4.0.1 land surface model within the NASA Land Information System (LIS). Afterwards, a series of hypothetical sensors with different orbital configurations is prescribed in order to retrieve snow, soil moisture, and vegetation. The ground track and footprint of each sensor is approximated using the Trade-space Analysis Tool for Constellations (TAT-C) simulator. A space-time subsampler predicated on the output from TAT-C is then applied to the synthetic truth. Furthermore, a hypothesized amount of observation error is injected into the synthetic truth in order to yield a realistic synthetic retrieval for each of the hypothetical sensor configurations considered as part of this dissertation. The synthetic retrievals are then assimilated into the NoahMP-4.0.1 land surface model using different boundary conditions from those used to generate the synthetic truth such that the differences between the two sets of boundary conditions serve as a realistic proxy for real-world boundary condition errors. A baseline Open Loop simulation where no retrievals are assimilated is conducted in order to evaluate the added utility associated with assimilation of one (or more) of the synthetic retrievals. The impact of the assimilation of a given suite of one or more retrievals on land surface model estimates of snow, soil moisture, vegetation, and runoff serve as a numeric laboratory in order to assess which sensor(s), either separate or in a coordinated fashion, yield the most utility in terms of improved model performance. The results from this OSSE show that the assimilation of a single type of retrieval (i.e., snow or soil moisture or vegetation) may only improve the estimation of a small part of the snow-soil moisture-vegetation system, but may also degrade of other parts of that same system. Alternatively, the assimilation of more than one type of retrieval may yield greater benefits to all the components of the snow-soil moisture-vegetation system, because it yields a more complete, holistic view of the coupled system. This OSSE framework could potentially serve as an aid to mission planners in determining how to get the most observational “bang for the buck” based on the myriad of different sensor types, orbital configurations, and error characteristics available in the selection of a future terrestrial freshwater mission.