# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 4 Discussion
Separate ER model fits for the whole dormant season did not improve ER estimates, but future work could investigate this using separate model fits specifically during periods with snow cover (similar to [PERSON] et al. [18]) or using LST as input to model peat temperature during the winter. Of the two types of NEE models tested, the non-linear regression model performed generally better than the model that was calculated by subtracting GPP from ER. This might be due to error propagation [16]. It is challenging to model NEE directly, as it is the difference between two large fluxes driven by separate processes with different seasonaldynamics. Although the largest under- or overestimations were found during the peak growing season, our results indicate that small differences during the whole year greatly influence the annual NEE as well. The modelling results showed that site-specific parameterization usually gave higher agreement with the EC fluxes than the average parameters from the LOOCV runs. However, for upscaling purposes, an average parameter set is more applicable than parameters that are strictly associated with one site. To reduce the importance of the regression parameters, other variables, that are able to explain the differences between the years within a site and also between the sites, could be included in the models. We tested several variables to scale GPP and ER in order to capture differences in the growing season fluxes between the sites, but none of them were strongly related to peak GPP or ER. The NDWI-based water scalar was selected for GPP modelling, as in general, WTD is one of the most important variables regulating the function of peatland ecosystems [55]. Measures of soil moisture availability or precipitation have also been used previously to model ER [21], but our initial tests found that including the NDWI or \(\mathrm{W_{s}}\) in the ER model did not improve the fit. NDWI is a spectral index expressing the influence of soil moisture on the vegetation rather than an actual soil moisture index, and it does not fully capture the seasonal variations in water table depth within a site (see Figure S2) or differences between sites (Table S2). Further work is needed to find remote sensing variables better able to capture variations in water table depth or soil moisture at peatland sites; for this purpose, hyperspectral and synthetic aperture radar data have shown promising results [56; 57]. Alternatively, the \(\mathrm{CO_{2}}\) flux modelling could be combined with hydrological modelling to further investigate how, for example, water table depth changes or snow melt timing affect the fluxes. For instance, [PERSON] et al. [58] assimilated regional soil moisture network data, remote sensing data, and high-resolution land surface parameters to develop an empirical soil moisture model. Our modelling approach investigated to what extent a single set of parameters is sufficient to upscale the \(\mathrm{CO_{2}}\) fluxes from a variety of northern peatlands. Future work could examine which improvements may be gained by modelling the fluxes of different peatland types (i.e., bogs and fens) separately. For example, our ER model with average parameters performed worst at Abisko-Stordalen, the only ombrotrophic bog in our study. Similarly, [PERSON] et al. [15] found differences in the relationships between remote sensing vegetation indices and peatland \(\mathrm{CO_{2}}\) fluxes depending on the presence of trees and peatland type. Bogs and fens have also been shown to respond differently to changes in water table and air temperature [59; 60] due to their contrasting vegetation assemblages. Improving the GPP and ER estimations separately would also improve the NEE model estimation. An alternative approach to estimate NEE could be a multivariate linear regression model including all remote sensing and environmental variables that are available at a site.