Accurately estimating net primary production (NPP) for various forest types on a large scale is of great significance to the global carbon cycle and climate change, particularly in terms of monthly variations. Most studies focus on the NPP estimation of individual tree species or a single forest type, and few studies explore the NPP estimation of multiple forest types simultaneously. Here, we aimed to explore the potential of the physiological principles predicting growth (3-PG) model to estimate the NPP of six typical tree species in Northeast China. Forest NPP was estimated on the basis of the 3-PG model using the fractional vegetation cover and leaf area index derived from moderate-resolution imaging spectroradiometer sensors. In addition, the monthly variation in forest NPP and factors influencing the NPP were analyzed. The results demonstrate that the proposed approach can yield reliable NPP estimates, and the determination coefficient (R2) between the estimated results and those obtained using the existing MODIS products was between 0.4010 and 0.5462. The forest NPP peaked approximately in July and was zero from October to April. Furthermore, the analysis of environmental effects on NPP indicated that temperature and site nutrition are the dominant forest growth factors, whereas available soil water is a limiting factor. Overall, we demonstrate that the proposed methodological framework satisfactorily estimated the NPP of the six typical tree species and has significant potential for forest growth prediction in China.