# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Remote Sensing Data  
The models to estimate GPP, ER, and NEE were driven by vegetation indices based on Sentinel-2-retrieved data along with LST data from MODIS.  
Sentinel-2 MSI images covering an area of 100 by 100 km at each site from 2017-2019 were downloaded from the European Space Agency (ESA) Copernicus Sentinels Scientific Data Hub. The Sen2 Cor processor (version 2.8.0) was used to perform atmospheric corrections and obtain land surface reflectance and scene classification [40]. The vegetation index EVI2 [14], with a spatial resolution of 10 m, was calculated using red (\(RED\)) and near-infrared (\(NIR\)) reflectances:  
\[\text{EVI2}=2.5\frac{NIR-RED}{NIR+2.4 RED+1}. \tag{1}\]  
A vegetation index related to water content, the Normalized Difference Water Index (NDWI) [41], was calculated using 20 m resolution \(NIR\) and short-wave infrared (\(SWIR\)) bands:  
\[\text{NDWI}=\frac{NIR-SWIR}{NIR+SWIR}. \tag{2}\]  
Both data sets were gap-filled and smoothed pixel-wise using a spline function in TIMESAT to produce daily values (Table S1).  
In order to select the NDWI and EVI2 pixels that fell within the flux source area, the EC flux footprint was estimated at each site with the Flux Footprint Prediction (FFP) model [28]. The model was driven by 30-min micrometeorological data (collected as part of the EC measurements, see Section 2.2) to produce daily footprint climatologies. The footprint climatologies were used to calculate weighted daily means of the EVI2 and NDWI within 80% of the footprint area. Furthermore, the time-series of the mean daily EVI2 and NDWI were both scaled so that the index value was 0 when GPP was 0. This procedure enabled the combination and comparison of data from all the study sites, as the base values of the indices were usually not equal to zero and varied from site to site.  
As NDWI expresses the effect of soil hydrology on the vegetation, NDWI was used to calculate a water scalar (\(\text{W}_{\text{s}}\)), following [PERSON] et al. [42]. The \(\text{W}_{\text{s}}\) represented the variations of moisture conditions interannual and between the sites. Other scalars that were tested are presented in Table S2. A simple version of the water scalar by [PERSON] et al. [43] was selected for the further analysis:  
\[\text{W}_{\text{s}}=1+\text{NDWI}_{\text{max}}, \tag{3}\]  
where \(\text{NDWI}_{\text{max}}\) is the 99 th percentile of NDWI during the growing season. During the dormant season, \(\text{W}_{\text{s}}\) was set to 1. The growing season was defined for each site and year based on when the daily mean air temperature (\(\text{T}_{\text{air}}\)) exceeded +5 \({}^{\circ}\)C for seven consecutive days [9].  
The LST data was acquired from the MODIS instruments mounted on the Terra and Aqua satellites. The MODIS LST products (MOD11A1, MYD11A1) include daily daytime and nighttime LST at 1 km spatial resolution, providing a maximum of four LST measurements per day. LST data were smoothed and gap-filled in the same way as the flux data and spectral indices using TIMESAT (Table S1). Due to the coarse resolution of the MODIS data, the footprint model was not used with these data, but we extracted data from one pixel where the EC tower and the peak of the flux footprint was located at each site. Daytime LST was used in the rest of the analysis due to the higher number of high quality images and a better fit with ground LST measurements that were available from some of the sites (R\({}^{2}\) = 0.85, RMSE = 5.3 \({}^{\circ}\)C for Degero 2017-2019 and R\({}^{2}\) = 0.75, RMSE = 6.1 \({}^{\circ}\)C for Abisko-Stordalen 2017-2019).