## 4 Discussion  
### GPP Estimation Based on GCC  
The GCC can be used to quantitatively describe the vegetation canopy greenness in alpine meadows on the Tibetan Plateau, and the CInorm derived from GCC can be used to estimate the vegetation GPP. This was consistent with the finding that the GCC can quantify the total GPP of the grassland and deciduous broad-leaved forest [11]. In this study, we found that GPP_CInorm has the highest accuracy, followed by GPP_EVI, and GPP_NDVI was the worst among them. This consequence illustrates that long-term phenological photos can replace remote sensing data thereby obtaining higher-precision regional GPP. In addition, we found that there was no deviation in the GCC peak value obtained from the AR station, but GCC had systematic deviation in spring and autumn. However, previous studies reported that the GCC peak date in growing season is much different from observed GPP in forests and grasslands [21]. The GCC peak value of the phenology photo of the TKY site (deciduous broad-leaved forest) and the TKC site (evergreeniferous forest) in Japan appeared about 20-30 days before the GPP peaks [26]. GCC and GPP of the Tongyu (meadow steppe) site in the Songnen Plain of China and the Maodeng Ranch (typical steppe) site in Inner Mongolia showed consistent and noticeable seasonal changes. GCC and GPP had large values during the growing season, and the values during the dormant period were very close to zero. The peak of GCC of meadow steppe  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Date/Experiment** & **GPP** & **R\({}^{2}\)** & **MAD** & **RMSE** \\ \hline \multirow{6}{*}{LUE model} & 2017 & GPP\_CInorm & 0.69 & 2.06 & 2.46 \\  & **2018** & **GPP\_CInorm** & **0.78** & **1.64** & **1.95** \\  & 2017 & GPP\_NDVI & 0.37 & 2.45 & 3.29 \\  & 2018 & GPP\_NDVI & 0.47 & 2.41 & 3.28 \\  & 2017 & GPP\_EVI & 0.54 & 2.18 & 2.83 \\  & 2018 & GPP\_EVI & 0.59 & 2.19 & 2.84 \\ \hline \multirow{6}{*}{Machine Learning} & Training & GPP\_train1 & 0.67 & 1.28 & 1.61 \\  & Training & GPP\_train2 & 0.54 & 1.61 & 1.96 \\ \cline{1-1}  & Training & GPP\_train3 & 0.62 & 1.45 & 1.77 \\ \cline{1-1}  & **Training** & **GPP\_train4** & **0.73** & **1.15** & **1.47** \\ \cline{1-1}  & Validation & GPP\_valid1 & 0.73 & 1.34 & 1.69 \\ \cline{1-1}  & Validation & GPP\_valid2 & 0.66 & 1.49 & 1.83 \\ \cline{1-1}  & Validation & GPP\_valid3 & 0.69 & 1.41 & 1.74 \\ \cline{1-1}  & **Validation** & **GPP\_valid4** & **0.75** & **1.25** & **1.56** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical indicators of the GPP from eddy covariance with the GPP simulation of the remote sensing GPP model and machine learning from 26 April to 30 September 2017 and from 26 April to 30 September 2018.  
lagged behind the peak of GPP by about ten days, and the peak of GCC of typical steppe lagged behind by GPP by about 20 days [44].  
The advantages of GPP estimated by GCC include high continuity of phenology photos and that phenology photos can be obtained multiple times a day with less affects from clouds. In addition, GCC will be affected by some factors when estimating GPP, such as the exposure rate of the photo, the spatial scale of the photo, the height angle of the sun, the presence of shadows or animals in the photo, temperature, and photosynthesis. These factors will affect the values of GCC and thus GPP estimation. To our knowledge, there have been few studies on the use of phenological photos (GCC) to estimate GPP on the Tibetan Plateau. Therefore, the performance of GCC in GPP estimation for alpine meadow on the Tibetan Plateau was tested in this study.