Forest’s net primary productivity (NPP) is a key index in studying interactions of climate and vegetation, and accurate prediction of NPP is essential to understand the forests’ response to climate change. The magnitude and trends of forest NPP not only depend on climate factors (e.g., temperature and precipitation), but also on the succession stages (i.e., forest stand age). Although forest stand age plays a significant role on NPP, it is usually ignored by remote sensing-based models. In this study, we used remote sensing data and meteorological data to estimate forest NPP in China based on CASA model, and then employed field observations to inversely estimate the parameter of maximum light-use efficiency (ε<sub>max</sub>) of forests in different stand ages. We further developed functions to describe the relationship between maximum light-use efficiency (ε<sub>max</sub>) and forest stand age, and estimated forest age-dependent NPP based on these functions. The results showed that ε<sub>max</sub> has changed according to forest types and the forest stand age. For deciduous broadleaf forest, the average ε<sub>max</sub> of young, middle-aged and mature forest are 0.68, 0.65 and 0.60 gC MJ<sup>-1</sup>. For evergreen broadleaf forest, the average εmax of young, middle-aged and mature forests are 1.05, 1.01 and 0.99 gC MJ<sup>-1</sup>. For evergreen needleleaf forest, the average ε<sub>max</sub> of young, middle-aged and mature forests are 0.72, 0.57 and 0.52 gC MJ<sup>-1</sup>.The NPP of young and middle-aged forests were underestimated based on a constant ε<sub>max</sub>. Young forests and middle-aged forests had higher ε<sub>max</sub>, and they were more sensitive to trends and fluctuations of climate change, so they led to greater annual fluctuations of NPP. These findings confirm the importance of considering forest stand age to the estimation of NPP and they are significant to study the response of forests to climate change.