## 2 Materials and Methods
### Input Spatial Data  
To bring the flux tower data into proper context and permit upscaling to the U.S. Great Plains, a wide range of input spatial data were selected and implemented in the regression tree model training (Section 2.4 below). Model development and mapping application at the weekly time step of NEP helped capture seasonal variations of NEP that are related to both weather and management activities. These input data sets provided the models with samples of how NEP behaves in accordance with variability of various spatial and temporal environmental characteristics. The first group of input spatial data used in model training included weekly eMODIS NDVI [26] and a simple temporal dataset with values ranging from 1 to 52 for the week of the year. NDVI is derived from visible and near-infrared (NIR) light reflectance measurements (Equation (1)) and correlates with the photosynthetic potential of vegetation [27]. The eMODIS NDVI product [26] is processed using the same data source and atmospheric correction algorithms as the standard MODIS collection 5 data product [28]. However, the eMODIS process uses a compositing algorithm which is largely dependent on maximum value-NDVI compositing, scan angle, and data quality flags to filter through input reflectance with bad quality, negative values, clouds, snow cover, or low view angles. Additional eMODIS NDVI quality measures were performed to reduce possible cloud contamination and anomalous atmospheric effects by applying temporal smoothing using a moving window weighted least-squares regression method [29]. While NDVI is a widely accepted vegetation index (VI) that has numerous applications [11; 14; 30], some disadvantages have been noted, such as spectral signature saturation in areas of high canopy density [31]. The weekly NDVI and temporal datasets were used to train the models on how NEP corresponds to weekly vegetation vigor. \[\mathrm{NDVI}=\frac{\mathrm{NIR}-\mathrm{\,\,red}}{\mathrm{NIR}+\mathrm{\,\, red}} \tag{1}\]  
The second set of spatial input data was selected to allow the models to capture how NEP responds to weekly meteorological-based characteristics. These included weekly total precipitation (PCP), weekly mean minimum and maximum air temperature (TMIN and TMAX), and weekly mean PAR acquired from the National Weather Service (NWS) National Centers for Environmental Prediction (NCEP) [32]; 30-year normals for annual TMIN, TMAX, mean temperature (TMEAN), and PCP for the 1981 to 2010 time period were also incorporated into the model. These normals were acquired from the PRISM Climate Group [33] and rescaled from 800 m to 250 m using bi-linear interpolation. The third set of input spatial data used in model training included several annual vegetation phenology metrics to train the models on how NEP fluctuates as a function of varying seasonality and states of vegetation. Specifically, nine unique annual phenology metrics, derived using methodology in accordance with [PERSON], et al. [34], were incorporated into the models. The phenology metrics included Amplitude (AMP), Duration (DUR), End of Season NDVI (EOSN), End of Season Time (EOST), Maximum NDVI (MAXN), Maximum NDVI Time (MAXT), Start of Season NDVI (SOSN), Start of Season Time (SOST), and Time Integrated NDVI (TIN). All phenology metrics were acquired from the U.S. Geological Survey (USGS) Remote Sensing Phenology (RSP) website [35]. The fourth group of input spatial data used in model training included four static soil variables, derived from the Soil Survey Geographic (SURGO) database (mostly mapped at the 1:24,000 scale), where available, with gaps filled from the State Soil Geographic (STATSGO) database. These soil datasets were acquired from the U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS) [36]. The four soil variables were available water capacity (AWC), clay percentage (CP), bulk density (BD), and soil organic carbon (SOC) within a 0-30 cm soil depth zone.