## 3 Results and Discussion
### Inconsistency in Sensor View Imposed Significant Variability RapidEye VI Model Performance  
Our results from the entire 3 year period do not support the hypothesis that VI generated from higher spatial resolution data should be a better predictor of ecosystem carbon uptake (Table 2). In fact, using higher spatial resolution data from RapidEye surprisingly decreased model performance at the control site in some cases, and did not significantly improve model performance at the girdled site. Some of this can be explained by the inconsistent scene to scene sensor view angle in our RapidEye time series, which influenced the apparent fractional cover of vegetation (Figure 4A). In an effort to test whether or not the variability in view angle was imposing noise into the RapidEye data, we binned the analysis by sensor view angles <7 \({}^{\circ}\)C off nadir, given the majority of our data were collected at more than 7 degrees off nadir (60%, Figure 4B). Limiting the analysis to scenes with absolute sensor view angle <7 degrees significantly improved model fits in NDVI RapidEye mods (at the girdled site (R\({}^{2}_{\text{adj}}\) increased from 0.14 to 0.77), yet decreased model performance at the control site; Table 5, Figure 5), potentially due to the changing apparent fraction of under-story and inter-canopy vegetation as a function of sensor view angle, which has a more pronounced impact at the girdle site relative to the control due to the lower fractional cover of overstory vegetation post manipulation. The inclusion of view angle into the model hierarchy as a parameter resulted in model over-fitting given the small sample size. The high inter-annual variability in system state (e.g., monsoonal precipitation or severe drought) at these sites further confounded our ability to account for view angle effects, due to the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Sensor** & **Model** & **R\({}^{2}_{\text{adj}}\)** & \(\Delta\)**AICc** & **ReLL** & **Weights** \\ \hline \multirow{6}{*}{Control} & Landsat & NDVI\({}_{\text{LS}}\) & 0.815 & 0 & 1 & 0.899 \\  & Landsat & NDVI\({}_{\text{LSW}}\) & 0.698 & 4.414 & 0.11 & 0.099 \\  & RapidEye & NDVI\({}_{\text{IRE}}\) & 0.795 & 22.327 & 0 & 0 \\  & RapidEye & NDVI\({}_{\text{IREW}}\) & 0.292 & 12.08 & 0.002 & 0.002 \\  & RapidEye & NDRE & \(-\)0.042 & 15.56 & 0 & 0 \\  & RapidEye & NDRE\({}_{\text{W}}\) & 0.5 & 30.359 & 0 & 0 \\ \hline \multirow{6}{*}{Girdle} & Landsat & NDVI\({}_{\text{LS}}\) & 0.592 & 4.36 & 0.113 & 0.04 \\  & Landsat & NDVI\({}_{\text{LSW}}\) & 0.677 & 1.575 & 0.455 & 0.161 \\ \cline{1-1}  & RapidEye & NDVI\({}_{\text{IRE}}\) & 0.711 & 0.234 & 0.89 & 0.315 \\ \cline{1-1}  & RapidEye & NDVI\({}_{\text{REW}}\) & 0.662 & 2.097 & 0.35 & 0.124 \\ \cline{1-1}  & RapidEye & NDRE & 0.717 & 0 & 1 & 0.354 \\ \cline{1-1}  & RapidEye & NDRE\({}_{\text{W}}\) & 0.672 & 8.709 & 0.013 & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance statistics for only year 2011 of the experiment, a significant drought year. See Table 1 for a description of model names. R\({}^{2}_{\text{adj}}\) is the adjusted R\({}^{2}\), \(\Delta\)AICc is the difference in model AICc relative to the lowest scoring model, ReLL is the relative log likelihood, or how likely it is that each model is actually the best, and Weights is a list of the AICc weights for each model. Previous work indicates the effects of sensor view angle on the fractional cover of various feature types (e.g., [50], pertaining to composites derived from the advanced very high radiometric resolution spectrometer (AVHRR)), and generally near-nadir view angles are a selection criteria for multi-image or multi-sensor analyses (e.g., [51]).