## 4 Conclusions  
Our results are promising in that we can use simple linear models to estimate GPP in both disturbed and undisturbed PJ woodlands driven by remotely sensed datasets. While structurally sensitive, NDVI is more informative to models of GPP than NDRE except during periods of extreme stress or disturbance. Similarly, we only saw a significant improvement in model performance using NDWI at the girdled site, during the manipulation event that took place in the Fall of 2009. Finally, the use of the RapidEye data did slightly improve estimates of GPP in both the control and girdled sites relative to Landsat ETM+, however this was only true when we reduced the variability in scene to scene sensor view angle in our RapidEye time series. This apparent advantage of the RapidEye data may be due to a combination of factors, including spatial resolution (5 m pixels _vs._ 30 m pixels) and spectral sensitivity of the sensor. While this may not play a strong role in more homogeneous, closed canopy systems, sensor view angle in this study often imposed more variability on NDVI than natural seasonal variability. Consequently, we recommend that remote sensing efforts to model VI sensitive processes in heterogeneous, low fractional cover systems, place high constraints on acquisition angles for time series, or bin analyses by viewing angle to minimize the potential confounding effects.  
We recognize that the temporally resolved RapidEye data set we utilized for this study is not a common commodity and currently carries with it a large cost. Using red-edge data added sensor and illumination geometry complexity, but did improve estimates of GPP during periods of ecosystem stress despite it. Our results suggest high resolution, red-edge employing platforms will potentially be very useful for resolving changes in canopy function during periods of rapid disturbance or recovery where LAI may be changing slowly in relation to chlorophyll content. The recently launched Sentinel-2 satellite mission will allow this to be tested on a broader scale by providing greater spatial and temporal resolution than Landsat, as well as the ability to calculate NDRE and NDWI, and be freely available. Secondly, the upcoming soil moisture active passive sensor (SMAP) may provide either direct measurements, or modeled estimates of soil moisture, providing further predictive power to estimate carbon uptake rates in semi-arid ecosystems.  
This research was supported primarily by the Office of Science (BER), US Department of Energy. We also acknowledge the funding from grant/cooperative agreement number 08 HQGR0157 from the United States Geological Survey (USGS) via a subaward from AmericaView, the National Science Foundation through funds to the Sevilleta LTER site and NM EPSCoR, in addition to the funding from the USFS for support of the flux towers, and from the New Mexico Space Grant Consortium (NMSGC) for the partial funding of satellite data acquisitions. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Office of Science, US Department of Energy. Finally, many thanks to [PERSON] for his work in processing the eddy-covariance data and sound advice throughout the project.  
[PERSON] conducted the primary data analysis and contributed in the eddy covariance instrumentation management, as well as crafting the manuscript. [PERSON], [PERSON], and [PERSON] advised on and contributed to the data analysis, as well as provided remote sensing expertise, and significant editing of the manuscript. [PERSON] contributed to the RapidEye data processing, initial data mining, as well as editing the manuscript. [PERSON] provided all of the eddy-covariance data, advised on research question generation, and reshaping of the manuscript.  
The authors declare no conflict of interest.