Beef cattle farming is a significant activity in Brazil, and forage quality has a direct impact on animal performance. However, traditional methods for estimating dry mass, which involve cutting, drying and weighing plant material, are slow and labor-intensive. UAVs equipped with multispectral sensors, such as the DJI Mavic 3M, offer a faster and more scalable alternative for monitoring mixed-forage pastures. This study estimates the dry mass of forage mixtures using multispectral UAV data in two scenarios: (i) using only spectral information and (ii) combining spectral data with canopy height measured in the field. Model performance was evaluated using R², RMSE, and percentage error. The multispectral-only model explained 55% of dry mass variability (720.56 kg/ha; 23.67%), while adding canopy height improved performance to 80% and reduced the error to 589.41 kg/ha (19.36%). Results show that canopy height enhances the accuracy and operational potential of UAV-based methods for estimating dry mass in mixed-forage areas.