## 4 Discussion
### Factors Affecting the Seasonal Variations of \(R_{\rm s}\)  
The spatial scales of MODIS data were widely apart, i.e., 500 m for vegetation indices and 1000 m for LST, respectively. As the average spatially-distributed observed \(R_{\rm s}\) data represented the mean value of the study site determined by analysis of spatial heterogeneity [25; 26], the MODIS products (i.e., LST and NDVI) can be considered to be consistent with the in situ measured data (i.e., \(R_{\rm s}\), \(T_{\rm s}\), and \(M_{\rm s}\)) in spatial matching. Moreover, some previous studies have verified the spatial matching of MODIS spatial products and the observation of eddy flux towers [23]. The seasonal variations in \(R_{\rm s}\) may be related to temporal changes in temperature, soil moisture, and other soil physicochemical factors (i.e., SOC, N, pH, etc. ), as well as changes in the phenology of biotic factors (i.e., plant photosynthesis and its development phase, fine root production, microbial activity, and below-ground C allocation) [14; 19; 30; 31; 33]. Soil physical and chemical factors were basically constant on temporal scale in the study site. We selected the most influential environmental factors to model seasonal variations in \(R_{\rm s}\). The capacity of the exponential model to explain the same independent environmental variables was significantly higher than that of the linear model. Based on the \(R^{2}\), RMSE, and AIC, a statistically significant difference was not detected among well fitted exponential-type functions considering \(T_{\rm s}\) alone, \(T_{\rm s}\) and \(M_{\rm s}\), or \(T_{\rm s}\), \(M_{\rm s}\) and NDVI together. The soil water supply was generally sufficient in the study site; therefore, adding it into the fitting model did not significantly improve the model explanatory capacity. Moreover, the addition of NDVI did not greatly improve the explanation capacity, possibly because of the lower above-ground biomass in the study site. The exponential function driven by \(T_{\rm s}\) was selected as the best fitted model to estimate the seasonal variations of \(R_{\rm s}\) in the study site as it needed only one unknown independent variable. This finding indicated that the contribution of the soil moisture and vegetation index to \(R_{\rm s}\) was relatively small and temperature exerted the greatest control on \(R_{\rm s}\) in the study site. In another alpine meadow on the Tibetan Plateau, [PERSON] et al. [34] found that soil temperature and above-ground biomass rather than soil moisture mainly affected the ecosystem respiration. Findings of the present study were in agreement with those of previous studies that showed \(R_{\rm s}\) was most sensitive to the soil temperature among all related environmental factors on a temporal scale [8; 13; 35], on account of the principle that soil temperature acted as a dominant climatic factor on \(R_{\rm s}\) alongside impact substrate and physiological activities, which ultimately affected \(R_{\rm s}\). Therefore, temperature could be a reliable predictor for the seasonal variation in \(R_{\rm s}\). However, several studies have shown that \(R_{\rm s}\) was most closely related to the soil moisture under dry conditions [36]. It depends on which factor is the limiting factor in any given ecosystem. In the present study, \(M_{\rm s}\) was sufficient, so its impact on \(R_{\rm s}\) was smaller. Therefore, the decisive driver of \(R_{\rm s}\) was not the same under different environmental conditions. The unexplained \(R_{\rm s}\) seasonal variation based on our selected model may have been due to the microbial properties and soil physical and chemical properties related to \(R_{\rm s}\), since the addition of \(M_{\rm s}\) and NDVI did not significantly improve the explanatory power.