# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 3 Results
### GPP and ER Models  
For GPP, the LOOCV parameterized linear regression model (Equation (4)), with the average coefficient \(a=0.70\) (Table S5), resulted in high agreement between modelled and EC-derived GPP at all the sites (R\({}^{2}\) = 0.54-0.78, RMSE = 0.42-0.98 umol m\({}^{-2}\) s\({}^{-1}\), Table 2). Based on the normalized root mean square error (NRMSE), the best fit was found at Abisko-Stordalen, where the error was 10% of the range of the EC-derived GPP, whereas the worst fit was found at Mycklemossen (NRMSE = 20%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Flux** & **R\({}^{2}\)** & **RMSE (μmol m\({}^{-2}\) s\({}^{-1}\))** & **NRMSE (\%)** \\ \hline \multirow{4}{*}{SE-Sto} & GPP & 0.76 & 0.42 & 10 \\  & ER & 0.23 & 0.41 & 19 \\  & NEE (Equation (9)) & 0.59 & 0.26 & 10 \\  & NEE (ER–GPP) & 0.16 & 0.37 & 15 \\ \hline \multirow{4}{*}{FI-Lom} & GPP & 0.78 & 0.98 & 12 \\  & ER & 0.68 & 0.62 & 14 \\ \cline{1-1}  & NEE (Equation (9)) & 0.57 & 0.79 & 12 \\  & NEE (ER–GPP) & 0.59 & 0.77 & 12 \\ \hline \multirow{4}{*}{SE-Deg} & GPP & 0.68 & 0.48 & 13 \\  & ER & 0.56 & 0.41 & 16 \\ \cline{1-1}  & NEE (Equation (9)) & 0.34 & 0.31 & 11 \\ \cline{1-1}  & NEE (ER–GPP) & 0 & 0.50 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Goodness-of-fit statistics for the predicted GPP, ER, and NEE, using the LOOCV parameterized models, for all available site years. NRMSE is RMSE normalized using the range (maximum–minimum) of the EC-derived flux. Figure 3: Exponential regression between MODIS daytime LST and EC-derived ER at all the sites and years. For ER, the agreement between the LOOCV parameterized model fit (Equation (8), see Table S6 for parameters) and the EC-derived ER differed between the sites more than the GPP model fit: R\({}^{2}\) varied between 0.23-0.85, and RMSE was between 0.31-0.98 umol m\({}^{-2}\) s\({}^{-1}\) (Table 2). The NRMSE of the ER model fit was similar to that of the GPP model, varying from 10% to 20%. The ER model performed best at Siikaneva, whereas the weakest relationships were found at Abisko-Stordalen and Mycklemossen. Using site-specific parameters (Table S4), the model NRMSE was reduced to between 7-12% of the flux range for both the GPP and ER estimates (Table 3). The time-series of the modelled and EC-derived fluxes (Figure 4 for GPP, Figure 5 for ER) show that the LOOCV parameterized models are able to capture the seasonal variation of the fluxes. However, some noticeable under- and overestimations of GPP and ER occur during the peak of the growing season. The LOOCV parameterized ER model also tended \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Fl-Sii} & GPP & 0.73 & 0.59 & 15 \\  & ER & 0.85 & 0.31 & 10 \\  & NEE (Equation (9)) & 0.33 & 0.39 & 15 \\  & NEE (ER–GPP) & 0 & 0.54 & 20 \\ \hline \multirow{4}{*}{SE-Myc} & GPP & 0.54 & 0.93 & 20 \\  & ER & 0.51 & 0.98 & 18 \\  & NEE (Equation (9)) & 0 & 0.41 & 15 \\  & NEE (ER–GPP) & 0 & 0.51 & 19 \\ \hline \multirow{4}{*}{Average} & GPP & 0.70 & 0.68 & 14 \\  & ER & 0.56 & 0.54 & 15 \\ \cline{1-1}  & NEE (Equation (9)) & 0.34 & 0.43 & 13 \\ \cline{1-1}  & NEE (ER–GPP) & 0 & 0.54 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)