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The spatially explicit model of leaf litter can help to understand its dispersal process, which is very important to predict the distribution pattern of leaves on the surface of the earth. In this paper, the spatially explicit model of leaf litter was developed for 20 tree species using litter trap data from the mapped forest plot in an evergreen broad-leaved forest in Tiantong, Zhejiang Pro- vince, eastern China. Applicability of the model was analyzed. The model assumed an allometric equation between diameter at breast height (DBH) and leaf litter amount, and the leaf litter declined exponentially with the distance. Model parameters were estimated by the maximum likelihood method. Results showed that the predicted and measured leaf litter amounts were significantly correlated, but the prediction accuracies varied widely for the different tree species, averaging at 49.3% and ranging from 16.0% and 74.0%. Model qualities of tree species significantly correlated with the standard deviations of the leaf litter amount per trap, DBH of the tree species and the average leaf dry mass of tree species. There were several ways to improve the forecast precision of the model, such as installing the litterfall traps according to the distribution of the tree to cover the different classes of the DBH and distance apart from the parent trees, determining the optimal dispersal function of each tree species, and optimizing the existing dispersal function.