## 4 Discussion
### GPP Estimation Based on Remote Sensing Vegetation Indices  
The remote sensing data had great advantages of wide coverage and high spatiotemporal resolution, which can be used to estimate GPP for regional and even global over a long period of time. However, the complexity of cloud cover, snow, moisture, temperature, and vegetation growth will all affect the accuracy of GPP estimation by remote sensing [45]. We used the remote sensing vegetation index MOD09 GA NDVI and EVI to estimate the GPPs of the AR station on an annual scale. For NDVI, we found that R\({}^{2}\) was 0.50 and RMSE was 2.78 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) in 2017; R\({}^{2}\) was 0.57 and RMSE was 2.76 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) in 2018. For EVI, R\({}^{2}\) was 0.85 and RMSE was 1.65 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) in 2017; R\({}^{2}\) was 0.87 and RMSE was 1.56 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) in 2018 (Appendix A, Figure A2). [PERSON] estimated the GPPs of AR station in 2009 and reported that R\({}^{2}\) of LUE models were up to 0.8 at AR station at annual scale [46]. This is consistent with the performance of the LUE model in this study. This study found that at the AR station, using EVI to estimate GPP was better than using NDVI. This was also reported by a previous study [47]. Compared with NDVI, the blue band was used to correct the background effects when calculating EVI. This difference may be one of the reasons that EVI had a better correlation with GPP than NDVI [48]. These findings were consistent with conclusions that the remote sensing GPP model can accurately estimate GPP in alpine meadows.  
The GPP_NDVI and GPP_EVI estimated by the LUE model based on the MOD09 GA vegetation indices fluctuated greatly during the growing seasons, which were lower than the GPP from eddy covariance (Figure 9). GPP_NDVI and GPP_EVI had many cloud contaminated data points close to zero during the growing season, which may be caused by some noise points in the MOD09 GA product. For GPP_NDVI, the number of cloud-contaminated data in the growing season of 2017 year was 56, R\({}^{2}\) of GPP_NDVI and GPP from eddy covariance was 0.22, the number of without cloud contaminated data was 92, and the R\({}^{2}\) of GPP_NDVI and GPP from eddy covariance was 0.61. The number of cloud contaminated data in the growing season of 2018 year was 49, the R\({}^{2}\) of GPP_NDVI and GPP from eddy covariance was 0.49, the R\({}^{2}\) of GPP_NDVI and GPP from eddy covariance was 0.26, the number of without cloud contaminated data was 99, and the R\({}^{2}\) of GPP_NDVI and GPP from eddy covariance was 0.60. For GPP_EVI, the number of cloud contaminated data in the growing season of 2017 year was 48, R\({}^{2}\) of GPP_EVI and GPP from eddy covariance was 0.40, the number of without cloud contaminated data was 100, and the R\({}^{2}\) of GPP_EVI and GPP from eddy covariance was 0.64. The number of cloud contaminated data in the growing season of 2018 year was 46, the R\({}^{2}\) of GPP_EVI and GPP from eddy covariance was 0.43, the number of without cloud contaminated data was 102, and the R\({}^{2}\) of GPP_EVI and GPP from eddy covariance was 0.71. For the non-growth seasons, because all the alpine grasses were withered and photosynthesis was weak, GPP_NDVI and GPP_EVI were very small and close to zero, which was consistent with the GPP from eddy covariance (Appendix A, Figure A2).