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Accurate quantification of forest aboveground biomass (AGB) is the foundation to the responses of diverse forest ecosystems to the changing climate. Lidar-based statistical models have been used to accurately estimate AGB in large spatial extents, especially in boreal and temperate softwood forest ecosystems. However, the few available models for temperate hardwood and hardwood-dominated mixed forests are low in accuracy due both to the deliquescent growth form of hardwood trees and the strong site-to-site variations in height-diameter relationship. In this study, we established multiplicative nonlinear regression models that incorporated both lidar-derived metrics and soil-based site productivity classes (high and low productivity sites) to estimate aboveground biomass in temperate hardwood forests. The final optimized model had high accuracy (R² = 0.81; RMSE = 45.5 Mg ha^− 1) with reliable performance …