# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 3 Results
### GPP and ER Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Flux** & **R\({}^{2}\)** & **RMSE (umol m\({}^{-2}\) s\({}^{-1}\))** & **NRMSE (\%)** \\ \hline \multirow{4}{*}{SE-Sto} & GPP & 0.85 & 0.33 & 8 \\  & ER & 0.86 & 0.17 & 8 \\  & NEE (Equation (9)) & 0.70 & 0.22 & 9 \\  & NEE (ER–GPP) & 0.55 & 0.27 & 11 \\ \hline \multirow{4}{*}{Fl-Lom} & GPP & 0.89 & 0.69 & 8 \\  & ER & 0.93 & 0.30 & 7 \\  & NEE (Equation (9)) & 0.75 & 0.60 & 9 \\  & NEE (ER–GPP) & 0.64 & 0.73 & 11 \\ \hline \multirow{4}{*}{SE-Deg} & GPP & 0.69 & 0.47 & 12 \\  & ER & 0.80 & 0.27 & 11 \\  & NEE (Equation (9)) & 0.42 & 0.29 & 11 \\  & NEE (ER–GPP) & 0.06 & 0.38 & 14 \\ \hline \multirow{4}{*}{Fl-Sii} & GPP & 0.88 & 0.39 & 10 \\  & ER & 0.91 & 0.24 & 7 \\ \cline{1-1}  & NEE (Equation (9)) & 0.56 & 0.32 & 12 \\ \cline{1-1}  & NEE (ER–GPP) & 0.24 & 0.42 & 16 \\ \hline \multirow{4}{*}{SE-Myc} & GPP & 0.82 & 0.58 & 12 \\  & ER & 0.81 & 0.61 & 11 \\ \cline{1-1}  & NEE (Equation (9)) & 0.03 & 0.39 & 15 \\ \cline{1-1}  & NEE (ER–GPP) & 0 & 0.62 & 23 \\ \hline \multirow{4}{*}{Average} & GPP & 0.83 & 0.49 & 10 \\ \cline{1-1}  & ER & 0.86 & 0.32 & 9 \\ \cline{1-1}  & NEE (Equation (9)) & 0.49 & 0.37 & 11 \\ \cline{1-1}  & NEE (ER–GPP) & 0.01 & 0.48 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Goodness-of-fit statistics for the site-specific GPP, ER and NEE models, for all available site years. NRMSE is RMSE normalized using the range (maximum–minimum) of the EC-derived flux. to underestimate the wintertime ER fluxes, especially at Lompolojankka and Mycklemossen (Figure 5c,i). Using the models with site-specific parameters helped better capture the peak of the fluxes, with less improvement in the general seasonal shape. The time-series of the LOOCV parameterized modelled GPP for Mycklemossen and Lompolojankka show a delayed start of the growing season compared to the EC-derived GPP (Figure 4c,j). Similar effects can be observed at Abisko-Stordalen in 2019 and Degero in 2017 (Figure 4a,e). As noted above, the LOOCV parameterized GPP and ER models performed poorly at Mycklemossen, and Figures 4i and 5i show that both fluxes were significantly underestimated at this site. Several environmental variables related to GPP and ER were tested in order to find a scaling factor that could help to improve the ability of the models to capture the growing season peak of the fluxes (Table S2), but no strong correlations were found. The NDWI-based water scalar (\(\text{W}_{\text{s}}\)) was selected for GPP modelling, as it improved the agreement between modelled and EC-derived GPP at Lompolojankka and Mycklemossen. The influence of this variable was small at the other sites, and it did not improve the accuracy of modelled ER (Table S3). Figure 4: _Cont._  
Figure 4: **(a,c,e,g,i)** Daily time series of EC-derived GPP (EC), modelled GPP using the LOOCV parameterization (RS joint), and modelled GPP with the site-specific parameters (RS site); (**b,d,f,h,j**); EC-derived GPP versus modelled remote sensing GPP. Black solid line is the 1:1 line, black dashed lines are the 1:2 and 2:1 lines. Figure 5: _Cont_.