Snow accumulation is a seasonally evolving process that results in a reflective, insulating cover over the Earth’s land mass, provides water supply to billions of people and supports numerous ecosystems. Despite being a critical component of the global water cycle, no satellite currently provides global snow mass data at the frequency, resolution and accuracy needed. The Earth’s terrestrial snowpack evolves throughout the year, affecting various regions, elevations and latitudes differently at different times. The observational needs and sensing capabilities also change in different locations and times. Seasonal snow is an ideal candidate for an optimized observational strategy that leverages existing sensors and focuses future mission concepts on monitoring the most critical areas to provide cost-effective and robust information. In this project we merge multiple technologies to develop a hypothetical experiment and demonstrate a potential snow observing strategy that utilizes diverse data to improve basin-wide SWE and streamflow forecasts. We assess the value of new potential sensors, such as from commercial smallsats to fill observing gaps and provide higher frequency observations during critical time periods. We ran this hypothetical experiment over the Missouri River Basin during two winter seasons, 2019 and a delayed near-real time simulation in 2025. We developed metrics to trigger taskable observations in locations where traditional monitoring satellites have limited sensing capabilities. We use the Tradespace Analysis Toolkit for Constellations (TAT-C) to generate orbital tracks from the existing satellites (AMSR2) and hypothetical taskable satellites. Results show that even without complete coverage, taskable observations show improved basin-wide SWE estimates.