## 3 Results
### Effects of Environmental Factors on Seasonal GOSIF and NDVI Values  
To examine the environmental controls on alpine vegetation, the controlling effects of environmental factors (FDs, Temp, Pre, and Grad) on GOSIF and NDVI values in both 2001-2009 and 2010-2018 were analyzed and shown in Figure 9. In each period, the GOSIF and NDVI values showed different relationships with the environmental factors at different seasonal scales. Among these environmental factors, the number of frost days had negative relationships with GOSIF and NDVI values. The air temperature had positive relationships with GOSIF and NDVI values. Other environmental factors, such as precipitation and radiation, had opposite effects in the two periods. By comparison, the correlation coefficient between the GOSIF-FDs and NDVI-FDs during 2001-2009 was greater than that during 2010-2018. This indicated that the relationship between GOSIF  
Figure 8: Spatial distributions of the trends in the average GOSIF and NDVI values from 2001 to 2018 in the Third Pole: (**a-h**) the trends of seasonal GOSIF and NDVI values over the period of 2001–2009, respectively; (**i-p**) the trends of the seasonal GOSIF and NDVI values over the period of 2010–2018, respectively. The seasonal cycles used in GOSIF and NDVI calculation correspond to those used in frost estimation. FDs and NDVI-FDs weakened from 2001 to 2018. Similarly, the difference between the coefficients of the two periods was used to evaluate the effects of environmental factors on the variation in the interannual correlation coefficients. During the two periods, declines in sensitivity were also seen in both GOSIF-FDs and NDVI-FDs. Similarly, during the two periods, declines in sensitivity were also seen in both GOSIF-Temp and NDVI-Temp. The difference between GOSIF-Pre and GOSIF-Grad was greater than that between NDVI-Grad and NDVI-Pre, indicating that the sensitivity of GOSIF and NDVI was divergent owing to the functional and structural characteristics of the vegetation. In terms of seasonal scales, temperature was one of the major controlling factors of the vegetation, followed by frost, with precipitation and radiation having the least influence. Considering that the seasonal change in alpine vegetation in the Third Pole is the result of the comprehensive action of many meteorological factors, no single factor explained the change to a high degree. This study indicated greater variability in the correlation coefficients between GOSIF and environmental factors compared to NDVI during the growing season. Notably, the differences in correlation coefficients between environmental factors and vegetation parameters were significant. Furthermore, the GOSIF product was superior to the NDVI in detecting frost impacts. Figure 9: The correlation coefficients between environmental factors and vegetation indicators in the Third Pole from 2001 to 2018. The solid bars indicate the 2001–2009 period (**a–d**). The bars with diagonal lines indicate the 2010–2018 period (**e–h**). FDs: frost days; Temp: air temperature; Pre: precipitation; Srad: shortwave radiation. To identify which environmental factors control alpine vegetation, we further compared the absolute values of these correlation coefficients for each pixel, reclassified four groups, and obtained the spatial distribution of the controlling factors in the growing season in the Third Pole from 2001 to 2018, as shown in Figure 10. Overall, large spatial heterogeneity existed in the temporal evolution of controlling factors. The discrepancies in controlling factors among the different vegetation datasets mainly resulted from the differences in the data. Most of the central plateau was mainly controlled by temperature, which was most obvious in spring. The northern Qinghai Plateau was controlled by frost days, which was most significant in the growing season, according to the trends and magnitudes of the frost days from 2001 to 2018. The southwest and northeast regions of the plateau were affected by radiation.