To better manage forage productivity and quality, it is important to accurately and objectively monitor legume content in grasslegume pastures. Current nondestructive methods are too subjective or labor intensive. Recent research has led to developments in digital image analysis (DIA) and remote sensing techniques useful in pasture research and management. Four nondestructive sampling techniques were compared with traditional botanical hand separations to determine their ability to predict legume content in pastures of tall wheatgrass [Thinopyrum ponticum (Podp.); TW] and alfalfa (Medicago sativa L.): TW-alfalfa; and in pastures of Old World bluestem [Bothriochloa bladhii (Retz) S.T. Blake; OWB], alfalfa, and yellow sweetclover (Melilotus officinalis L.; YSC): OWB-legume. Each sampling procedure better estimated and predicted legume content in OWB-legume pasture (Rcal2 = 0.40 to 0.78; Rpred2 = 0.40 to 0.78) than in TW-alfalfa (Rcal2 = 0.02 to 0.61; R2 pred = 0.08 to 0.61). Overall, the best predictive accuracy was obtained with the PowerPoint model in OWB-legume pasture. Only the visual model showed potential in TW-alfalfa because these two species were difficult to distinguish on-screen by DIA methods or using spectral thresholds. The use of nondestructive sampling techniques to measure legume content is promising for mixed pastures when the grass and legume components are distinct hues of green. Although the need for more research is recognized, the ultimate goal is to apply these findings to automated scanners that offer producers rapid measurements of legume content in mixed pastures.