## 4 Discussions
### Mismatches of Long-Term Variations and LSP among LAI/FVC/GPP  
We find a significant positive tendency on surface vegetation over the northeastern and northwestern TRSR during the last 20 years, which is consistent with one recent study [9]. However, there exists a large mismatch between FVC with other two products, regardless of LSP detection, interannual trend, and correlation. These phenomena may be caused by the influences of combinations of various factors, such as the algorithms and effects of climate or other environmental factors. Firstly, all these biophysical variables are typical indicators used to characterize the growth status of green vegetation; FVC and LAI can also be linked by the Beer-Lambert equation, theoretically [54; 55]. Yet, FVC is not a proxy of LAI or GPP, as environment and human activities may restrain the process of green vegetation carbon sequestration over a long period. A recent study conducted in the Amur River basin also indicates a much weaker rise in GPP growth rate when compared with the FVC, partly due to the influences of environmental factors (e.g., warming-induced drying) [3]. In addition, current retrieval algorithms of those products are mostly designed to acquire a single variable from remote sensing observations. Thus, various parameters are retrieved from different models and schemes based on various physical assumptions [56]. These parameter-specific approaches ignore the physical connections between the various datasets, resulting in a difference among different time-series biophysical products even if they are estimated from the same remote sensing data. Lastly, we observe good agreements between LAI and GPP in the TRSR. The reason may be explained by LAI having significant effects on the GPP estimations in the revised EC-LUE model [33; 57]. More specifically, the absorbed photosynthetically active radiation (APAR) by green leaves is an important parameter in the revised EC-LUE model, while LAI is critical in the calculation of APAR. Note that we find the LSP metrics and long-term changes of the three products perform better on homogeneous surfaces (i.e., high RVP level) and flat terrain (i.e., low TNI level). This phenomenon may be explained by the fact that the pixels with high RVP or TNI values are usually located at high altitudes in the TRSR, and the climatic warming-induced drying may significantly affect vegetation growth and carbon uptake at high elevations [20]. In addition, the estimation of vegetation phenology at the pixel level remains a large uncertainty and can reach approximately \(\pm 20\) days, especially in high-elevation areas [44]. Both the effects of species composition [45] and the frequency and mixture of species present in each pixel [46] contribute to high variability in phenological estimations. More specifically, the spatial resolution of satellite products used in our study is 500 m, and the pixels are generally not composed of a homogeneous ground biome type, but a mixture of several classes. The pixels, therefore, are not a single but mixed reflections of spectral features of several ground communities [58]. The heterogeneous pixels may have a significant negative effect on the accurate retrieval of global biophysical products, whereas the current algorithms of GLASS products do not consider the mixed characteristics of pixels. On the other hand, the quality of satellite products becomes poor as the topographic complexity increases. Firstly, local topography may cause the frequent occurrence of climate change so that reflectance data in these regions often receive contaminations from aerosols, clouds, and snow [59]. Fortunately, the surface reflectance data are reprocessed to remove the influences of extreme climatic change before inputting the algorithm [35]. The rugged surfaces also influence the irradiance and bidirectional reflectance distribution function (BRDF) of optical satellite images [60]. Significant differences exist in the solar irradiance received by areas with different slopes.