Abstract: Rapid and accurate measurement of leaf area (LA) and leaf dry mass (LM) is one of the basic requirement for leaf geometry and plant functional studies. It is important not only for studying leaf morphology and biomass estimation, but also for understanding the response mechanism of vegetation to climate change. In this study, we took two temperate deciduous broad-leaved tree species, Ulmus laciniata and Acer tegmentosum, as the study objects, constructed empirical models between LA or LM and leaf structure parameters (e.g., leaf length, L; leaf width, W) to reveal the interspecific variability in the selection of empirical model formats (linear or non-linear) and independent variables. We evaluated the forecast accuracy of these empirical models in predicting LA and LM for each species. The results showed that the optimal empirical models for predicting LA were LA=0.614L^1.468W^0.464 and LA=0.865(LW)^0.933, and for predicting LM were LM=0.003L^1.537W^0.365 and LM=0.001L^2.318 for U. laciniata and A. tegmentosum, respectively. The forecast accuracies of empirical models in predicting LA were 88% and 96%, and for LM were 73% and 83% for U. laciniata and A. tegmentosum, respectively. In addition, based on the empirical models for predicting LA and LM, the specific leaf area also could be indirectly measured under non-destructive conditions, with the forecast accuracies being 83% and 90% for U. laciniata and A. tegmentosum, respectively. These results provide a technical support for the efficient measurement of leaf traits and their dynamics.