AbstractMonitoring snow cover in prairie environments is important for understanding water and energy fluxes, agricultural production, and flooding, but difficult due to shallow snowpack and considerable snow heterogeneity. Cosmic ray neutron sensors (CRNS) are sensitive to snow within a radius of 150–250 m, which allows for continuous estimation of snow water equivalent (SWE) over a large footprint and may better represent area‐averaged snow cover in prairies than conventional SWE instruments, such as snow pillows. A CRNS was installed at Montana State University's Central Agricultural Research Center (CARC; 47.06°, −109.95°) in Moccasin, MT in coordination with NASA's SnowEx 2021 field campaign. This work assesses the feasibility of a CRNS for SWE monitoring in prairies by comparing CRNS SWE estimates to spatially distributed SWE derived from uninhabited aerial vehicle lidar snow depths within the sensor's footprint and manual snow pit measurements. Lidar observations show snow cover was highly spatially variable, with the largest snow accumulation near barriers and the least in barren fields. Additionally, we evaluate our CRNS SWE estimates using Ultra Rapid Neutron Only Simulation (URANOS) Monte Carlo simulations. Comparisons of SWE estimates derived from lidar, CRNS, and URANOS for shallow snowpack at the site yielded root mean square values of about 2 mm (approximately 30% of the mean SWE). These results suggest that the CRNS is effective at integrating over significant spatial variability within its footprint at this site. However, the spatial distribution of snow exerts a strong influence on the CRNS signal and must be considered when interpreting CRNS observations.