## 3 Results and Discussion  
### Models of GPP in PJ Woodlands Had the Lowest Error at the Disturbed Site  
Simple linear models driven by photosynthetically active radiation (PAR), air temperature (TA), and remotely sensed VI, summarized in Table 1, were able to explain between 80% and 90% of the variability of gross GPP in both intact and disturbed PJ woodlands (e.g., Table 2). However, the strength of this relationship was contingent on a tight coupling between the variability in VI and canopy \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **VI Combination** & **Scale (m)** \\ \hline NDVI\({}_{\text{LS}}\) & Landsat NDVI & 30 \\ NDVI\({}_{\text{LSW}}\) & Landsat NDVI, NDWI & 30 \\ NDVI\({}_{\text{IRE}}\) & RapidEye NDVI & 5 \\ NDVI\({}_{\text{REW}}\) & RapidEye NDVI, NDWI & \(5,30\) \\ NDARE & RapidEye NDRE & 5 \\ NDARE\({}_{\text{W}}\) & RapidEye NDRE, NDWI & \(5,30\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model framework description. Each model is structured as GPP \(\sim\) PAR \(\times\) TA \(\times\) VI where VI is represented by the corresponding Table entry above, while PAR and TA are derived from on-site instrumentation. Acronym definition: GPP (gross primary productivity), PAR (photosynthetically active radiation), TA (air temperature), VI (vegetation index), NDVI (normalized difference vegetation index), NDRE (normalized difference red-edge index), NDWI (normalized difference wetness index). physiological processes that affect a given index. For this reason, the model was run on individual years, as well as on all years together. Overall, the best performing model VI at the undisturbed PJ woodland was Landsat ETM+ derived NDVI, whereas at the disturbed site the red-edge VI performed best (R2 adj of 0.90 and 0.92, respectively). When we drove the model hierarchy with data from all three years in the study, the collection of models performed poorly across the board (Figure 2, Table 3), with the Landsat ETM+ based models performed best (R2 adj = 0.40 at control, R2 adj = 0.50 at girdle). We believe this to be due to the coupling strength between changes in canopy VI and canopy function during as a function of interannual variability in climate. As an example, the fit statistics from both the Landsat ETM+ and RapidEye driven models improved significantly when a single year (e.g., 2009) was used to drive the regression (Figure 3), dramatically increasing the fit of the simple Landsat ETM+ NDVI model at the control site (R2 adj from 0.40 to 0.90) and increased the fit of the RapidEye NDRE model at the girdled site (R2 adj from 0.14 to 0.92) (Table 2, discussed in following sections). At the control site, Fall 2009 was very productive, with a strong monsoon bringing sufficient moisture to the ecosystem, and consequently the coupling of Landsat ETM+ NDVI, PAR, and air temperature explained almost 90% of the variability seen in GPP. In the case of girdled site during the same period, the manipulation in piron mortality drove both the rapid decrease in VI, and the sudden decrease in canopy uptake of carbon. Both during this period of significant canopy stress and throughout the entire analysis period, the best model fits were seen at the girdled site, possibly due to the drastic decreases in LAI and subsequent increased coupling between the VI and GPP, previously documented as a potential effect of the manipulation itself ([PERSON] et al., 2016).