Groundwater-dependent ecosystems are biologically diverse and productive ecosystems but constitute a small fraction of total land area in semiarid regions. Efforts to link remotely sensed data from satellite-based platforms to measurements of vegetation structure and function at smaller spatial scales have increasingly received attention, due to the need to manage diverse landscapes at scales relevant to management. In the semiarid western United States, grazing is a dominant land use and meadows can receive a high degree of grazing pressure. In this study we examined satellite-based and near-surface imagery to determine if they were useful in assessing grazed systems with different grazing management. We compared meadows that were chronically grazed by feral horses in conjunction with periodic cattle grazing to a meadow managed and grazed by cattle only. We examined the agreement between near-surface digital cameras (PhenoCams) and satellite-based indices of greenness and production for meadows in the Central Great Basin, United States. We also verified them with field-collected data on percent foliar cover by dominant functional groups. There was strong agreement between the Landsat normalized difference vegetation index and PhenoCam Green Chromatic Coordinate (GCC) (Pearson's r ≥ 0.61). Gross primary production modeled using Landsat satellite imagery and integrated over the growing season had a strong linear relationship with GCC integrated over the growing season (R² = 0.89). Furthermore, despite differences in spatial and temporal resolution, integrated metrics from both platforms were able to discern differences in grazing pressure. Meadows with chronic feral horse grazing plus 3 mo of livestock grazing had reduced integrated gross primary production and GCC in comparison with a meadow that had short-term grazing (for 2 mo) by livestock only. The ability to detect differences in grazed systems from PhenoCams and satellite platforms provides important tools for quantifying the effects of grazing in groundwater-dependent ecosystems.