# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 5 Conclusions  
In this study, we demonstrated that regression models driven by remote sensing-derived data can be used to estimate CO\({}_{2}\) fluxes in northern peatlands (Abisko-Stordalen, 68\({}^{\circ}\)N; Lompolojankka, 68\({}^{\circ}\)N; [PERSON], 64\({}^{\circ}\)N; [PERSON], 62\({}^{\circ}\)N; [PERSON], 58\({}^{\circ}\)N) at relatively high accuracy. A strong relationship was found between GPP and the product of Sentinel-2 EVI2, Sentinel-2-derived water scalar (W\({}_{\text{s}}\)), and daytime LST from MODIS. ER was strongly related to MODIS LST, and including EVI2 as a proxy for GPP improved the agreement with eddy covariance-derived ER. NEE was successfully explained by MODIS LST in combination with Sentinel-2 EVI2 and W\({}_{\text{s}}\) at Abisko-Stordalen and Lompolojankka. At other sites, however, the relationship between modelled NEE and EC-measured NEE was weak. The regression models performed better when the model parameters were estimated separately for each site in comparison to the average parameters acquired with a Leave-One-Out-Cross-Validation (LOOCV) procedure, but the LOOCV parameterization is more appropriate for upscaling the CO\({}_{2}\) fluxes to a regional scale. We conclude that there is great potential to upscale GPP and ER from a site to the regional level using solely remote sensing-derived data. With high-resolution Sentinel-2 data, we were able to take advantage of a flux footprint model that maps the flux source area and thus minimize the uncertainty from mismatches in the scale of the EC and remote sensing data. It also enabled us to map the spatial variability of GPP within a peatland. In addition, there is an opportunity to develop high spatial and temporal resolution models of ER by downscaling coarse MODIS data to finer resolution or using the forthcoming Landsat Collection 2 LST product. Overall, modelling peatland NEE at regional scale still poses a challenge, and in order to fully utilize the potential of remote sensing data to upscale peatland CO\({}_{2}\) fluxes, further studies would be needed to examine the yet unexplained spatial and temporal variation of these fluxes. **Supplementary Materials:** The following are available online at [[https://www.mdpi.com/2072-4](https://www.mdpi.com/2072-4)]([https://www.mdpi.com/2072-4](https://www.mdpi.com/2072-4)) 292/13/4/818/s1: Section S1: Eddy covariance flux data processing, Section S2: The multivariate linear regression model for NEE, Table S1: TIMESAT spline parameters, Table S2: Coefficient of determination between the EC-derived GPP and ER, environmental data, and remote sensing-derived indices, Table S3: R\({}^{2}\) and NRMSE of ER models at each site, Table S4: Site-specific model parameters, Table S5: The prediction performance of the GPP model during the leave-one-out-cross-validation runs, Table S6: The prediction performance of the ER model during the leave-one-out-cross-validation runs, Table S7: The prediction performance of the NEE model during the leave-one-out-crossvalidation runs, Figure S1: Example of the relationship between EVI2 and observed GPP and LST and observed ER, Table S8: Annual cumulative NEE, Figure S2: Example of NDWI (Normalized Difference Water Index) and water table depth time series at Degero, Figure S3: Example of the linear regression model for NEE at Mycklemossen. Conceptualization: S.J., J.K., L.E. ; methodology: S.J., J.K., and L.E. ; formal analysis: S.J. and J.K.; resources: L.E. and N.K. ; data curation: M.A., L.K., A.L., M.B.N., J.R., E.-S.T., P.V., and P.W. ; writing--original draft preparation: S.J. ; writing--review and editing: all authors; visualization: S.J. and J.K.; supervision: L.E. and N.K. ; project administration: L.E. ; funding acquisition: L.E. and N.K. All authors have read and agreed to the published version of the manuscript. The study was financed by FORMAS grant 2016-01223 CarboScale to [PERSON] and by funding from the Centre for Environmental and Climate Science, Lund University. Not applicable.