## 5 Conclusions  
The GPP_CInorm has the highest accuracy, followed by GPP_EVI, and GPP_NDVI was the worst in the LUE model. However, it should be noted that there is a problem with the seasonal dynamics of CInorm results, manifested as a significant deviation existing in spring and autumn. In several sets of machine learning experiments, the accuracy of GPP was found to be the highest when both the CInorm based on phenological camera photo and MODIS vegetation indices (NDVI and EVI) are input at the same time, indicating that the two types of data can complement each other's defects and improve estimation accuracy of GPP. Compared with the GPP accuracy estimated by EVI and NDVI in the LUE model, the GPP accuracy estimated by combining multisource data is increased by 27% and 59%, respectively. These results illustrate that the overall accuracy of machine learning is significantly higher than the traditional LUE model. However, compared with the GPP accuracy estimated by CInorm in the LUE model, it is reduced by 3%, which is caused by the lower quality of GPP data from eddy covariance and the shorter time series of GCC data. Due to the late installation of phenological camera resulting in the quantity of photos available now is very small. Thus, we can get longer time series GCC data later, the purpose is to reduce the impact of the amount of data on the accuracy of machine learning estimation GPP. [PERSON] designed the research; [PERSON], [PERSON], [PERSON], and [PERSON] collected and processed data; [PERSON] and [PERSON] analyzed the data and wrote the draft; [PERSON] provided import input to the manuscript; [PERSON] and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key R&D Program of China (Grant No. 2017 YFA0604801), the National Natural Science Foundation of China (Grant No. 41771466), the Special Fund for Key Program of Science and Technology of Qinghai Province (Grant No. 2017-SF-A6), and the Youth Innovation Promotion Association CAS to X.W. (No. 2020422). [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Uncertainty in simulating regional gross primary productivity from satellite-based models over northern China grassland. _Ecol. Indic._**2018**, _88_, 134-143. [CrossRef]
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