## 4 Discussions
### Study Limitations  
When compared with previous studies that used MODIS datasets after 2000 to analyze vegetation variation [78; 79], this study used the GLASS GPP product and revealed the spatiotemporal characteristics of GPP in the GKM from 1982 to 2015. Obviously, an extension of the study period could provide much more information about the dynamic change of vegetation and the carbon cycle. However, some problems still existed. The precision of the GLASS-GPP time series in our study area needs to be verified with data observed in the eddy covariance flux tower, however, because of poor management, data acquired from the tower cannot be used in the verification, so supplementary analysis using the mainstream MODIS dataset is necessary for making a comparison. On the other hand, when compared with the distribution area of boreal forest (especially the bright coniferous forest), the spatial resolution of GLASS-GPP data that we used is a little coarser, and therefore details may be overlooked. For this reason, further studies combining with spatially finer TM datasets have been undertaken.  
Uncertainty in our correlation results may be caused by the interpolated meteorological data, the calculated phenology parameters and pixel mismatches (use of remotely sensed data from different sources). The meteorological observation station are sparsely distributed in the GKM which is the northernmost part of China, there are only five stations in our study area and the remaining stations that are distributed around the GKM are far away. Moreover, there are no stations in the surrounding northern and eastern direction. All of these factors may increase the uncertainty of the interpolation. There are also some limitations that are caused by the phenological parameters. Although the phenology parameters we used have been verified by many relative studies [54; 80],field observation data is still necessary for authenticity verification. Unfortunately, no field observation data is available for the GKM in the study period, so our work should be improved by further field observations. Pixel mismatches also caused some errors. Since we used remotely sensed data from different source, pixel mismatches were inevitable. For example, GLASS-GPP had a 5-km resolution, while NDVI had an 8-km resolution, so we resampled NDVI to match GPP data, which caused error in NDVI.  
This paper only considered five factors affecting the spatiotemporal dynamics of GPP; other factors are also important, especially anthropogenic activities and fire in the GKM. For example, the natural forest protection project launched in 1998 played an important role in our study area, as mentioned above. However, because such roles hard to quantify, it is difficult for us to know their specific effect on GPP. What is more, we cannot entirely separate the effect that is brought by anthropogenic activities and the quantified environment factors, which added some uncertainty to our study. Therefore, further studies should be concentrated on finding alternatives and analyzing effects brought by anthropogenic activities and fire. Further study is needed before we can thoroughly understand the potential mechanisms affecting the spatiotemporal pattern of GPP in the GKM.