Biomass is an important parameter that has a decisive influence on the final yield. Destructive measurements of biomass are time-consuming and labor-intensive. Proximal sensing methods using field spectrometers offer indirect observation and estimation of biomass. For this purpose, farmers' fields were investigated in a two-year growing season of rice and canopy reflectance was measured by spectrometers. Several vegetation indices (VIs) and multiple linear regression (MLR) models based on bands around the red-edge domain (680-760 nm) were tested. Published rededge VIs were generally prone to saturation, whereas MLR models and the Ratio of Reflectance Difference Index in the red-edge (RRDIre) were less influenced by saturation. The linearly tested MLR (based on VIs) and the RRDIre models provided the best performance for biomass estimation in model validation using an independent dataset.