## 3 Results and Discussion
### NDRE and NDWI Reduced Model Error only during Periods of Significant Stress  
In our linear model hierarchy, including NDRE did not significantly improve model performance relative to models that used only NDVI (Table 2). Previous work in this region suggests NDRE is more responsive to decreases in canopy chlorophyll concentration relative to NDVI, yet following a lag period (roughly 2 weeks), the NDVI performed equally well in quantifying foliar concentration of chlorophyll [34]. Given that our time series spanned multiple years, with rapid decreases in canopy chlorophyll occurring only at the girdled site in 2009, we ran the same model structures on only data from 2009, which captured the girdling process (Figure 3). Over this smaller time period of significant canopy stress, the NDRE dramatically improved model performance at the girdled site (R\({}^{2}\)adj from 0.136 for NDVI to 0.92 for NDRE, Table 2). However, we did not observe significant improvement in NDRE driven model fit at the control site, which is consistent with the idea that NDRE explains variability in canopy function best during early periods of rapid canopy change.  
The incorporation of Landsat ETM+-derived NDWI into the model structure did not improve model fit in either Landsat ETM+ or RapidEye based models when the entire time series was used in the regression (Figure 2, Table 3). NDWI did however slightly reduce prediction error in both Landsat ETM+ and RapidEye NDVI based models at the girdled site, in 2009 alone (Figure 3, Table 2). While the inclusion of the moisture index slightly improved model performance, the added complexity was penalized fairly heavily given the small number of observations used to parameterize the model.  
Figure 3: One to one plots of tower derived (measured) and predicted (modeled) gross primary productivity for the control (C, black) and girdled (G, grey) sites for 2009 only. See Table 1 for a description of model names.  
Previous work has shown that including Landsat ETM+ moisture indices can improve estimates of productivity in some dryland ecosystems [21; 46], although it appears to be less effective in sparse canopy systems [47]. Our data suggest NDWI may only improve estimates of productivity in semi-arid coniferous woodlands during rapid changes in canopy physiological status, such as during canopy mortality, yet do not provide sufficient evidence to justify increasing the complexity of the model structure in this case. Moisture sensitive VI are less effective at representing canopy water content during periods of low to moderate drought stress [48; 49], suggesting that GPP predictions during periods of the year when water is less limiting may not benefit from the inclusion of a canopy moisture VI as much as during periods of drought stress. This is evident at the girdle site during 2011, a year which was characterized by significant drought across the southwestern US. Model performance improved at the girdle site with the inclusion of Landsat ETM+ NDWI. However, the increased complexity of the model and our small sample size limit the generalizability of the finding that NDWI may constrain model uncertainty in these systems during drought periods (Table 4).