Genetic modification of plant lignin composition is an important strategy for improving biomass enzymatic digestibility without sacrificing the normal growth of the plant. However, the absence of fast and convenient methods for rapid determination of lignin composition has impeded corresponding research. Near-infrared reflectance spectroscopy (NIRS) analysis has potential as a solution for this dilemma, while the NIRS measurement for expediently assaying lignin composition in rice straws is still lacking. In this study, visible and near-infrared reflectance spectroscopy (VIS/NIRS) and modified partial least squares (MPLS) method were combined to develop calibration models for predicting the lignin monomer contents in a diverse rice population. Four optimal equations for predicting the content of p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) lignin units and their total amount (H + S + G) were generated with acceptable determination coefficients for calibration (0.85 to 0.93), cross-validation (0.75 to 0.88), and external validation (0.82 to 0.88), and the ratio performance deviation (RPD, 2 to 3.01). This study was the first to demonstrate that VIS/NIRS could give a sufficiently accurate prediction of lignin monomer contents in rice and could be applied for rapid assessments of large-scale rice straw samples.