## 2 Materials and Methods
### Calculation of Vegetation Indexes from PhenoCam  
Digital numbers (DNs) of each individual channel (R\({}_{\text{DN}}\), G\({}_{\text{DN}}\), B\({}_{\text{DN}}\) and NIR\({}_{\text{DN}}\)) were extracted from each photograph, and averaged over the different regions of interest (ROIs) (Figure 1). The overall brightness of each ROI (RGB\({}_{\text{DN}}\)) and the relative brightness of green channel, known also as green chromatic coordinates--GCC, were computed with Equations (1) and (2):  
\[\text{RGB}_{\text{DN}} =\text{R}_{\text{DN}}+\text{G}_{\text{DN}}+\text{B}_{\text{DN}} \tag{1}\] \[\text{GCC} =\frac{\text{G}_{\text{DN}}}{\text{RGB}_{\text{DN}}} \tag{2}\]  
CamNDVI was also computed in the different ROIs according to [PERSON] et al. [29], using the algorithm implemented in the \"phenopix\" R package [34; 36]:  
\[\text{CamNDVI}=\frac{\text{NIR}_{\text{DN}}{}^{\prime}-\text{R}_{\text{DN}}{ }^{\prime}}{\text{NIR}_{\text{DN}}{}^{\prime}+\text{R}_{\text{DN}}{}^{ \prime}}, \tag{3}\]  
where NIR\({}_{\text{DN}}\)' and R\({}_{\text{DN}}\)' are the adjusted exposure values of NIR\({}_{\text{DN}}\) and R\({}_{\text{DN}}\), respectively. For a detailed calculation and the exposure adjustment formula, please refer to [PERSON] et al. [29]. As the R\({}_{\text{DN}}\)' and NIR\({}_{\text{DN}}\)' are not direct measurements of reflectance, the CamNDVI values are not directly comparable to the NDVI from other data sources. [PERSON] et al. [29] found a linear relationship between CamNDVI and the NDVI derived from the radiometric sensor (ASD FieldSpec 3) using the bands of 750 nm for the NIR and 605 nm for the red. They suggested using the linear regression coefficients to adjust the CamNDVI values for comparability with NDVI from radiometers [29; 34].  
Therefore, we applied the method suggested by [PERSON] et al. [29] and [PERSON] et al. [34] to rescale CamNDVI using the NDVI derived from the Decagon SRS (VIs was calculated and averaged over a 30 min period to be consistent with VIs from PhenoCam). The coefficients and statistics of the linear regression used to compute the scaling factors are shown in Table 1. In the following only the rescaled CamNDVI values are used and presented.  
Likewise, the CamNIRv and CamRVI were also calculated with adopting Equations (4) and (5) which refer to [PERSON] et al. [41] and [PERSON] [42], respectively.  
\[\text{CamNIRv}=\text{CamNDVI}\times\text{NIR}_{\text{DN}}^{\prime}, \tag{4}\]\[\text{CamRVI}=\frac{\text{NIR}^{\prime}_{\text{DN}}}{\text{R}^{\prime}_{\text{DN}}}, \tag{5}\]  
where NIR\({}_{\text{DN}}\)\({}^{\prime}\) and R\({}_{\text{DN}}\)\({}^{\prime}\) are the adjusted exposure values like Equation (3). A similar approach used for the CamNDVI was used to compute CamNIRv and CamRVI: SRS-based NIRv and RVI were used to adjust the CamNIRv and CamRVI in order to make them comparable with the data derived from other sources (Table 1).  
The analysis was conducted on various ROIs as depicted in Figure 1: we selected ROIs with only trees, grass, and both (hereafter referred as Tree, Grass, and Eco ROI, respectively). The different sites have different tree/grass proportions in the camera FOVs, as only one direction of ecosystems could be captured from the PhenoCam. However, the fractional tree canopy covers were consistent (~0.20) in the four sites by referring to field surveys and the classification analysis using airborne hyperspectral imagery [58; 59]. The analysis of each footprint for each EC tower also indicates GPP is contributed from ~20% tree canopy and ~80% grasses [59]. In order to reduce the bias introduced by the different ratios of tree/grass in the images and camera FOV, which has to do with logistical constrains during the camera installation, the ecosystem VIs (GCC, CamNDVI, CamNIRv, CamRVI) were computed by using the weighted average of the VIs derived from Grass ROIs of 0.8 and Tree ROIs of 0.2.