## Data and Methodology
### Observational Data
Biome physiological parameters were specified with the use of a biome property look-up table (BPLUT), which was modified to agree with GPP derived from flux towers and synthesized net primary production [13]. While MODIS algorithm provides spatial patterns of GPP reasonably well and captures its temporal variability across various biome types [63], accurate GPP estimates over certain biomes are still difficult to achieve [64; 65; 66]. Recently, it was demonstrated that standard MOD17 GPP product substantially underestimates GPP over croplands [67; 68; 69], especially in summer, which is likely due to the prescribed LUE being too low [64; 70]. One limitation of MOD17 product is related to the fact that it does not take into account the influence exerted on the photosynthetic capacity and consequently GPP estimation by leaf quality, which is linked to leaf chlorophyll and nitrogen content [71]. We have employed Breathing Earth System Simulator (BESS) GPP data as an additional observational reference. The BESS GPP dataset used in this study is a global 8-day 1-km product derived with the use of the simplified process-based BESS model, MODIS land and atmosphere data, reanalysis datasets and ancillary data sources [72]. BESS GPP product has demonstrated performance similar to that of MODIS GPP when compared against FLUXNET data, and better consistency with GPP modeled by MPI-BGC [73]. Normalized Difference Vegetation Index (NDVI) is also provided by the MODIS instrument. In this study, we used a 16-day composite MOD13A2 product at \(1\times 1\) km spatial resolution. NDVI is an index calculated from the near-infrared and visible radiation reflected by vegetation  
\[NDVI=\frac{R_{NIR}-R_{red}}{R_{NIR}+R_{red}} \tag{4}\]  
where \(R_{NIR}\) is the reflectance in the near-infrared (NIR) region of the spectrum, and \(R_{red}\) is the reflectance in the red range of the spectrum. NDVI values typically vary from \(-1\) to \(1\), where zero \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site Name and Code** & **State** & **Latitude (\%)** & **Longitude (\%)** & **Land Cover** & **Period** \\ \hline ARM Southern Great Plains site-Lamont (US-ARM) & OK & 36.61 & –97.49 & CRO & 2003–2012 \\ ARM USDA UNI OSU Woodward Switagrass 1 (US-AR1) & OK & 36.42 & –99.42 & GRA & 2009–2012 \\ ARM USDA UNI OSU Woodward Switagrass 2 (US-AR2) & OK & 36.64 & –99.60 & GRA & 2009–2012 \\ Corral Pocket (US-Cop) & UT & 38.09 & –109.39 & GRA & 2001–2007 \\ Kansas Field Station (US-KFS) & KS & 39.06 & –95.19 & GRA & 2007–2012 \\ Mead rainfed maize-govhen rotation site (US-Ne3) & NE & 41.18 & –96.44 & CRO & 2001–2013 \\ Twitchell Corn (US-Tw2) & CA & 38.10 & –121.64 & CRO & 2012–2013 \\ Twitchell Alfafa (US-Tw3) & CA & 38.12 & –121.65 & CRO & 2012–2013 \\ Twitchell Island (US-Twt) & CA & 38.11 & –121.65 & CRO & 2009–2014 \\ Walnut Gulch Kendall Grasslands (US-WKG) & AZ & 31.74 & –109.94 & GRA & 2004–2014 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic information regarding the FLUXNET sites used in this study. GRA denotes grass and CRO denotes crop. indicates the absence of vegetation and values approaching 1 generally correspond to tropical rainforests with characteristic dense vegetation. As a measure of vegetation \"greenness\", NDVI has proved to be useful in monitoring seasonal and phenolic activity, as well as quantifying the duration of the growing season and leaf turnover, or 'dry-down', period [74]. A time integral of NDVI values over the growing season is highly correlated with net primary production (NPP) [75; 76; 77], and in some cases NDVI can be used as a surrogate estimator of NPP [78].