Forest understory vegetation contributes to aboveground and belowground carbon budgets and affects terrestrial ecosystem function, yet quantifying understory biomass at large spatial scales is difficult. Increasingly, forest management operations are aided by unmanned aerial vehicles (UAVs) that collect imagery and airborne light detection and ranging (LiDAR) data, potentially providing a dataset for understory monitoring. In this study, we evaluated the suitability of two different remote sensing datasets (A and B) for predicting understory biomass amounts (field average of 2 Mg ha-1) using five different machine learning models. We compared (1) Acquisition A LiDAR and multispectral data, (2) Acquisition A multispectral data only, (3) Acquisition B SfM photogrammetry point cloud and multispectral data, and (4) Acquisition B multispectral data only. Remotely sensed data were temporally linked to ground plots in open post-harvest conditions, where we employed a method for quick in-field biomass estimation via calibrated photos. Our best model was a stochastic gradient boosting model built with LiDAR and multispectral data (testing R2=0.43, training R2=0.67). The multispectral-only model from Acquisition A performed similarly (testing R2=0.44, training R2=0.53), despite larger bias in shaded areas, suggesting an alternative streamlined method for creating wall-to-wall estimates. Structure-for-motion was unfit for modeling biomass under all tested conditions, indicating the importance of data acquisition and post-processing. Our study provides a novel framework for evaluating low-volume understory biomass efficiently and creating landscape scale predictions using limited ground measurements and remotely sensed data.