## 5 Discussion  
### The Role of Remotely Sensed Phenology in the Biome-BGC Model  
Vegetation phenology strongly influences energy, carbon, and nitrogen cycling between the atmosphere and the terrestrial ecosystems. Accurate phenology simulation is a prerequisite for the accurate simulation of carbon fluxes. As mentioned earlier, the original meteorological-based phenology module in the Biome-BGC was found to significantly delay the SOS of alpine grasslands, which seriously affected the simulation accuracy of GPP. This limitation has also been discovered by [21], and its reason may be attributed to the fact that the moisture threshold and the temperature threshold in the original meteorological-based phenology module are not applicable to alpine regions like the TP. Specifically, the meteorological-based phenology module in the Biome-BGC assumes that leaf onset occurs when both the summed soil temperature and the summed precipitation are greater than the critical values [22]. However, for the TP, the seasonal thawing of frozen soil and snow cover can provide water for the germination and growth of alpine vegetation, which will make the original moisture threshold become inapplicable. Furthermore, the temperature threshold in the original phenology module of the Biome-BGC was derived from the empirical data for well-suited regions, which may also not well represent the vegetation growth on the TP. Therefore, improvements to the phenology module of the Biome-BGC is required.  
Figure 8: Spatial distribution of trends and significance levels of GPP of alpine grasslands on the TP from 1982 to 2015. (**a**) Trends of GPP; (**b**) Significance levels of GPP trends.  
Figure 9: Interannual variations of different factors of alpine grasslands on the TP from 1982 to 2015. (**a**) Mean annual GPP; (**b**) Mean annual precipitation; (**c**) Mean annual temperature.  
Due to the temporal and spatial continuity of remote sensing technology, it has been successfully used for the study of vegetation phenology on the TP [25; 26]. In this study, we have used the remotely sensed phenology to prescribe the phenological states within the Biome-BGC model. Results show that the remotely sensed phenology can more accurately represent the phenological characteristics of alpine grasslands than the original meteorological-based phenology module in the Biome-BGC. In addition, different from the previous studies that improved the phenology module of the Biome-BGC [19; 20], our study integrated the remotely sensed phenology with the Biome-BGC model, which is more suitable for applications over large areas. This strategy could also be extended to other process-based ecosystem models. However, some limitations of this strategy should also be mentioned. Because remote sensing is an earth observation technique, it can only observe vegetation phenology in a hindcasting manner. Therefore, the Biome-BGC model after a phenology module modification in this study is incapable of making future predictions based on future climate scenarios. At the same time, since NDVI is not sensitive to seasonal changes in most evergreens, this strategy is also not applicable to evergreen species. On the other hand, it is worth mentioning that [PERSON] et al. [63] proposed a multi-factor-driven prognostic phenology module for alpine meadows by combining temperature, precipitation, photoperiod, insolation, and snowfall, which could also be integrated into the Biome-BGC model to overcome the limitations in its original phenology module.