Technical Abstract: Spatiotemporal variability in forage quantity and quality requires regular assessment of the capacity for grasslands to support livestock nutritional requirements. Current methods for estimating grazing capacity are typically production-based and lack the forage quality data necessary to match nutrients in forage with livestock requirements in real time. The primary purpose of this paper is to describe a new model for estimating current grazing capacity using spectral data in Geographic Information Systems (GIS) for historically native pastures in North Dakota. We define grazing capacity as the number of days a specific pasture might support the nutritional requirements of beef cattle. The model is driven by livestock nutritional requirements and by estimates of forage quantity (phytomass) and quality (crude protein) available from the Landsat or Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite sensors. Since cattle consume unpredictable proportions of photosynthetically active vegetation (PV) and non-photosynthetically active vegetation (NPV), we designed two versions of the model: one using PV only, and one using total phytomass (PV + NPV). Our secondary purpose was to determine how the model output for the two model versions compared with actual cattle usage in a short-term field trial. The model using PV only underestimated grazing capacity by 4 days. The model using total phytomass (both PV and NPV) overestimated grazing capacity by 7 days. Results were similar using both Landsat and ASTER data. These initial results suggest the PV-only model may conservatively estimate cattle grazing capacity and support grassland sustainable management goals, but additional cattle trials are needed.