# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 3 Results
### NEE Models  
Out of the two NEE models, the non-linear regression model (Equation (9)) generally performed better than calculating the difference of modelled GPP (Equation (4)) and modelled ER (Equation (8)) when compared against EC-measured NEE (see Table 2 for LOOCV parameterized model prediction estimates and Table 3 for site-specific estimates). The site-specific model parameters (Table S4) gave higher agreement than the average parameters from the LOOCV (Table S7). Overall, the performance of the NEE models was weaker than the GPP and ER models, although at Abisko-Stordalen and Lompoljankka the non-linear regression NEE model with site-specific parameters gave similar agreement to that of GPP and ER, R\({}^{2}\) > 0.70, and NRMSE around 9% of the range of measured NEE (Table 3).  
Figure 5: (**a,c,e,g,i**) Daily time series of EC-derived ER (EC), modelled ER using the LOOCV parametrization (RS joint) and modelled ER with the site-specific parameters (RS site); (**b,d,f,h,j**) EC-derived ER versus modelled remote sensing ER. Black solid line is the 1:1 line, black dashed lines are the 1:2 and 2:1 lines.  
The time-series of modelled and measured NEE show that modelled NEE corresponds fairly well to measured NEE, and the seasonal shape of NEE was captured at most of the sites and years (Figure 6). However, the models have difficulties capturing the growing season peak, as seen for instance at Abisko-Stordalen in 2017 (Figure 6a), and the periods of positive NEE (when ER is higher than GPP) at the start and end of the growing season. The latter can be seen clearly at Lompolojankka in both years (Figure 6c). The poor performance of the GPP and ER models at Mycklemossen are also shown in the NEE time-series, where the modelled NEE corresponds weakly to the measured NEE (Figure 6i).  
Figure 6: _Cont._  
The comparison of the annual cumulative modelled NEE and measured NEE (Figure 7) emphasizes the importance of accurate flux modelling, as small over- or underestimations, not only during the peak growing season, but during the whole year, can result in a significant difference in the annual CO\({}_{2}\) budget. For example, the NEE models at Abisko-Stordalen in 2017 underestimated the NEE growing season peak equally (Figure 6a), but result in very different annual accumulation of CO\({}_{2}\). The model with the site-specific parameters suggests that the site is a CO\({}_{2}\) sink, whilst the model with the average parameters suggests that the CO\({}_{2}\) budget is close to zero (Figure 7). Table S8 shows that the modelled annual NEE differed largely from the measured annual NEE at most of the sites and years. The model was able to estimate the annual NEE accurately (maximum 3% difference to the measured NEE) at Abisko-Stordalen 2017 (site-specific estimate), Abisko-Stordalen 2018 (predicted with joint parameters), and Siikaneva 2017 (site-specific estimate). The linear regression model with site-specific parameters performed slightly better than the model with jointly estimated parameters at eight out of 13 site-years.  
Figure 6: (**a,c,e,g,i**) Daily time series of EC-derived NEE (EC), modelled NEE using the LOOCV parametrization (RS joint) and modelled NEE with the site-specific parameters (RS site); (**b,d,f,h,j**) EC-derived NEE versus modelled remote sensing NEE. Black solid line is the 1:1 line, black dashed lines are the 1:2 and 2:1 lines.