## 2 Data and Methods
### NDVI Preprocessing  
Each of the NDVI datasets were scaled to a spatial resolution of 0.125 \({}^{\circ}\)latitude by 0.125 \({}^{\circ}\)longitude and a temporal resolution of 10 days spanning the time period 1982 to 2008 except for SPOT, MODIS Terra and Aqua which were processed beginning 1999, 2001, and 2003 respectively. Depending on the initial spatial resolution and grid definition each dataset was scaled differently.  
The original NDVIg and NDVI3g data were upscaled from an 8 km Albers grid and 1/12 \({}^{\circ}\) geographic grid respectively to the 0.125 \({}^{\circ}\) grid through an averaging of all polygons that intersected each 0.125 \({}^{\circ}\) grid cell weighted by the degree area of each polygon inside each cell. The original SPOT and VIP were upscaled from a 0.01 \({}^{\circ}\)and 0.05 \({}^{\circ}\) geographic grid respectively to the 0.125 \({}^{\circ}\) grid using a true area upscaling function (area_hi2 lores) in NCAR Command Language (NCL) designed for geographically projected data. The MODIS Terra and Aqua data were upscaled from the 1 km Sinusoidal grid to the 0.125 \({}^{\circ}\) grid by taking the arithmetic average of the NDVI data from each MODIS grid cell that was inside each 0.125 \({}^{\circ}\) grid. This was deemed appropriate as there are over 100 MODIS grid cells per 0.125 \({}^{\circ}\)grid cell. Finally STAR was downscaled from 0.144 \({}^{\circ}\)to 0.125 \({}^{\circ}\) using bilinear interpolation.  
After all the datasets were scaled to the same spatial grid they were also interpolated to the same 10-day temporal grid using the fit grid tension spline algorithm (FITCRV) available in the NCAR Graphics library. Missing values in the middle of each year were filled using the interpolation spline from the remaining non-missing values, however, the 10-day period before the first non-missing value and after the last non-missing value in each year were set to missing to avoid extrapolation errors. While necessary for direct pixel-to-pixel comparisons, changing the temporal and spatial scale of these products could produce results different from each product's native resolution. Furthermore, the products being scaled to 10-days from longer time-periods (Terra--16 days, NDVIg/NDVI3g--bi-monthly, VIP--15 days) still should not be considered to have a temporal resolution higher than the original dataset.  
Finally, all data were seasonally filtered according to the missing data mask from the SPOT files. The seasonal filter was calculated by setting to missing any pixel for any 10-day period that was missing for that 10-day period for any of the years from 2000 to 2010 in the SPOT NDVI data. This provides a conservative seasonal mask for each pixel which is mostly dependent on the climatology of snow and cloud cover. The mask is applied to the upscaled NDVI data for all datasets, over all years.  
Following previous regional studies [25, 26, 27], we focus on the North American Monsoon region that can be defined as broadly existing from 90 \(\mathrm{\SIUnitSymbolMicro W}\) to 120 \(\mathrm{\SIUnitSymbolMicro W}\) and 15 \(\mathrm{\SIUnitSymbolMicro N}\) to 35 \(\mathrm{\SIUnitSymbolMicro N}\). To include more land cover types and climate regimes in our NDVI intercomparison, we expand the domain to cover the western and central United States and Mexico, extending from 85 \(\mathrm{\SIUnitSymbolMicro W}\) to 125 \(\mathrm{\SIUnitSymbolMicro W}\) and 15 \(\mathrm{\SIUnitSymbolMicro N}\) to 50 \(\mathrm{\SIUnitSymbolMicro N}\). It will be a future task to intercompare these NDVI products over global land.