# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 4 Discussion  
The results of this study showed that purely remote sensing (RS) derived data can be used in regression models to estimate seasonal CO2 uptake and release from northern peatlands. The product of EVI2, LST, and an NDWI-based water scalar (W\({}_{s}\)) explained a large proportion of the variability in GPP at all the sites. In addition, ER was strongly related to daytime LST, and based on these findings, NEE could be estimated from LST in combination with EVI2 and W\({}_{s}\). However, the accuracy of the estimated fluxes was inconsistent across the sites and years. It should be noted that the EC-derived fluxes vary at a high frequency that satellite remote sensing data cannot reproduce. Smoothing the time-series of EC-derived NEE, ER and GPP made them more comparable to the remote sensing data. Nevertheless, the RS models rely on LST to capture the daily variations in the fluxes, because LST is the only remote sensing dataset that varies significantly at a daily scale, whereas EVI2 and NDWI vary more slowly over the growing season. This means that short-term variability can only be captured at a coarse spatial scale. Some sites showed clear variations in the ER-LST and the GPP-EVI2 relationships between the spring green-up phase and the senescence phase, suggesting that each phase should be modelled separately. However, during initial testing, we found that separating the CO2 fluxes between different seasonal stages was not straightforward. A simple split of a flux time series between the green up phase and the senescence phase can cause problems with modelling the peak season accurately, and therefore we decided to model the whole year at once. Further analyses may reveal solutions to this challenge. The linear regression model for GPP that we developed is an empirical variation of the formal LUE model by [PERSON] [12]. LUE models usually include PAR. In this study, PAR was not included in the GPP model, as it showed very weak relationships with daily GPP at all the sites. Instead, the regression parameter, the water scalar W\({}_{s}\), and LST jointly provided a proxy for PAR and the LUE term, but these factors might not be sufficient to explain all spatial and temporal variations of the photosynthetic efficiency within a peatland ecosystem. Alternative ways to estimate the LUE coefficient using remote sensing methods, such as the photochemical reflectance index (PRI) [47], may solve this issue [48]. Unfortunately, the wavelength bands to calculate PRI are not available from Sentinel-2. Another remote sensing-derived variable that can be used to estimate GPP is solar induced fluorescence (SIF) [49], which will be operational at the ecosystem scale in future from the forthcoming Fluorescence Explorer (FLEX) satellite mission by ESA. LST explained ER well at all sites. Including productivity in the ER model (using EVI2 or modelled GPP) improved the model fit compared to the original [PERSON] and Taylor [44] model. [PERSON] et al. [46] argue that the [PERSON] and Taylor equation represents heterotrophic respiration, and including GPP is a way of including information related to autotrophic respiration in the estimate. It has been shown that ER is tightly linked to vegetation productivity [50; 51; 52], and a productivity or greenness index has also been used in remote sensing models of ER before [18]. Our results demonstrate that using EVI2 instead of modelled GPP (as proposed by [PERSON] et al. [46]) gives similar model accuracy whilst also making the model simpler and more robust. However, the LST and EVI2 based model might be less suitable to estimate ER in winter, since low soil surface temperatures and vegetation productivity predict low fluxes, while higher temperatures in deeper layers of peat enable decomposition to continue [53]. Wintertime ER can be an important part of the annual CO2 balance in northern peatlands that is possibly underestimated by surface temperature driven models [54].