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For model-based quantitative reconstructions of past vegetation cover on the scale of landscapes, pollen productivity estimates (PPEs) are key input parameters. In this study, we employed a random sampling strategy to collect moss polsters at 20 sites in Changbai Mountain, northeastern China. A detailed vegetation survey within 1000-m radius around each sampling point was carried out and digitized vegetation maps were used for vegetation data compilation. A forest map at the scale of 1:25,000 was used to extract information about vegetation for the area between 1000 and 5000 m from each sampling point. Using the ERV (Extended R-Value) model, pollen productivity was estimated for Larix , Pinus , Juglans , Ulmus , Tilia , Betula and Fraxinus relative to Quercus . Estimates of pollen fall speeds for the eight taxa as well as the relevant source area of pollen (RSAP) were also obtained. Three different ERV sub-models were tested against the data. The sub-model 3 produced the best goodness of fit and the PPE values calculated with this sub-model show that Betula (5.04), Pinus (3.11), Juglans (1.94) and Ulmus (1.40) are high pollen producers with higher PPEs than Quercus while Fraxinus (0.76), Larix (0.30), Tilia (0.16) are low pollen producers compared to Quercus . The high pollen producers are all anemophilous species, while low pollen producing plants include both entomophilous, such as Fraxinus , Tilia and anemophilous species such as Larix . The estimated RSAP for the eight tree pollen taxa is about 2000–2500 m. [ABSTRACT FROM AUTHOR]