Quantifying rangeland vegetation amounts with remotely sensed satellite data is a proliferating field of study. Yet the resulting datasets are rarely related to use-based monitoring indicators (i.e., utilization or residual biomass), which are critical for adaptive management and to inform the subsequent year's grazing plans. To better assess our ability to use remotely sensed data products for grazing monitoring and adaptive management, we tested the relationships between a variety of vegetation biomass metrics derived from remotely sensed data on a bunchgrass-dominated grassland in northeast Oregon and two common indicators: stocking rate at the pasture scale (40–250 ha; a management indicator) and field-based utilization estimates at the plot scale (25–50 m; a grazing indicator). At the pasture scale, we correlated stocking rate to biomass metrics and found two metrics that had consistent relationships to stocking rate: fall mean biomass (r values range: –0.52 to –0.56; P values < 0.001) and the 10th percentile of the relative difference between summer and fall biomass (r values range: –0.47 to –0.52; P values < 0.01). Scatterplots from these correlations were then evaluated alongside managers' knowledge to interpret why some pastures deviated from the overall pattern. At the plot scale, we correlated infield utilization estimates to biomass metrics and found consistent relationships with fall mean biomass (r values range: –0.32 to –0.47; P values < 0.001) and the relative difference between summer and fall biomass (r value: from –0.20 to –0.62; P values < 0.005). To further visualize the utilization correlations, we classified these two biomass maps into three categories guided by our utilization estimates. Significant changes in biomass due to management and interannual variation in biomass amounts stood out. The results and visualizations demonstrate how remotely sensed data relate to conventional grazing monitoring indicators and exemplify how remotely sensed data can be used to inform adaptive management.