## 3 Results  
### Regression Tree Model  
Model training accuracies for the crop and grass weekly NEP mapping algorithms are summarized in Table 2. The cropland NEP model training accuracy achieved a correlation coefficient (\(r\)) of 0.94, a RMSE of 1.01 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) a MAD of 0.60 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\). The grass model training accuracy had an \(r\) of 0.88, RMSE of 0.62 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), and a MAD of 0.45 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\). Given the large datasets (sample sizes of 1445-2277) used to develop the grass and crop regression trees. Autocorrelation effects are expected to be minor because the ordinary least squares estimators associated with the regression models are consistent under general conditions [64] and provide unbiased estimates [65]. The NEP regression tree mapping models for crops and grass are presented in Tables S2 and S3. All map legends and statistics were conformed to a common unit and are presented as NEP as g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) or g C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\). The regression tree mapping rules and prediction regressions are completely transparent and quantify the multiple sequential models (committee models) from which a mean prediction is made. The crop model had 3 committee models that had between 17 and 21 different prediction strata or rules, while the grass model had 5 committee models with 10 to 31 different rules (Tables S2 and S3). Each rule has stratification criteria (\"conditions\") as well as an associated multiple regression model (\"prediction\"), which is applied within a respective rule's strata. The usage of independent variables in the model development data base with the respective multiple regression models are for deriving the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Intercept** & **Slope** & \(p\) & \(r\) & **RMSE** & **MAD** \\ \hline \multicolumn{6}{c}{**Croplands**} \\ \hline Training & 0.018 & 0.018 b & \textless{}0.01 & 0.94 & 1.01 & 0.60 \\ Site cross validation & \(-\)0.098 & 0.675 b & \textless{}0.01 & 0.76 & 1.62 & 1.14 \\ Year cross validation & 0.127 a & 1.101 b & \textless{}0.01 & 0.8 & 1.79 & 1.02 \\ Hypothetical random test & na & na & na & na & na & 0.71 \\ \hline \multicolumn{6}{c}{**Grassland**} \\ \hline Training & \(-\)0.042 a & 1.180 b & \textless{}0.0001 & 0.88 & 0.62 & 0.45 \\ Site cross validation & 0.033 & 0.339 b & \textless{}0.0001 & 0.48 & 1.14 & 0.81 \\ Year cross validation & 0.226 a & 0.389 b & \textless{}0.0001 & 0.49 & 1.16 & 0.81 \\ Hypothetical random test & na & na & na & na & na & 0.53 \\ \hline \hline \end{tabular}  
* Significantly different from 0; b significantly different from 1; na, Not applicable.  
\end{table} 
Table 2: Summary of prediction accuracies of NEP (g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)) from model development (training), site cross validation, year cross validation, and a random test hypothetical projection. model \"predictions\". The number of weekly NEP flux tower observations in the model development data base that meet each rule's stratification criteria is quantified (the number of weekly NEP flux tower observations or cases in Tables S2 and S3), and the percentage utilization of the spatial data inputs for stratification (\"conditions\") can be quantified for each regression tree mapping model (see Attribute usage \"condition\", or \"model\" at the end of Tables S2 and S3). An overall utilization for each spatial input was estimated by the mean utilization across attribute condition and use in the regression model (Tables 3 and 4). The weekly eMODIS NDVI (proxy for photosynthetic potential of vegetation) was the most utilized spatial input variable in the crop model and PAR (primarily driven by day length and cloud cover) was the most utilized spatial variable in the grass model (Table S5).