The use of drones has increased in recent years for monitoring and managing rangelands. High-resolution cameras and improved sensors provide an opportunity to investigate pasture-scale sampling methodology as an operational approach to estimate forage mass on rangelands using canopy height models derived from drone data. Our objectives were (1) to compare double sampling and vegetation clipping methods with very fine 3D data derived from drone-based imagery, (2) to compare forage mass estimation between methods using different numbers of drone-derived samples, and (3) estimate time efficiency of each one of these methods. To accomplish this, we acquired drone imagery in a 1 060-ha pasture in the South Texas Plains ecoregion in June 2020. We used two different pixel sizes for the drone image acquisition: 1.5 cm (50 m above ground level [AGL]) and 3.0 cm (100 m AGL). We compared six forage mass sampling approaches: double sampling (DS-ground), vegetation clipping (VC-ground), drone-double sampling at 50 m (drone-DS50) and 100 m (drone-DS100) AGL, and drone-vegetation clipping at 50 m (drone-VC50) and 100 m (drone-VC100) AGL. We generated 100 and 500 digital samples per site (total 700 and 3500 digital samples) to compare our estimates. Simple linear regression analyses were used to evaluate relationships between drone derived vegetation volume and the forage mass derived from DS and VC. We compared three sampling sizes: 70 field-based quadrats, 700, and 3,500 digital samples. Drone-VC50 with 700 5818 ± 78 kg · ha^-1) and 3,500 (5653 ± 34 kg · ha^-1) samples provided the smallest forage mass estimations at a large-pasture scale. Number of samples.h^-1 increased from 22 to 52 with the DS methods and 1.2 to 38 with the VC methods. Our results suggest that a combination of DS and VC with drone data collection could be a reliable approach for future drone-based forage estimation.