# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 1 Introduction  
Peatlands are an important ecosystem for the global carbon cycle, as they cover only 3% of the terrestrial area but contain around 40% of global soil organic carbon [1]. The waterlogged and anoxic conditions in peatlands inhibit the decomposition of plant material, creating a long-term net sink of atmospheric carbon dioxide (CO2) and, consequently, an effective storage of organic carbon [2]. Peatlands assimilate CO2 through photosynthesis (gross primary productivity, GPP) and release it through autotrophic respiration (from vegetation) and heterotrophic respiration (microbial decomposition). The sum of theautotrophic and heterotrophic respiration is the ecosystem respiration (ER). The difference between ER and GPP is the net ecosystem exchange (NEE), which determines whether an ecosystem is a carbon sink or source (excluding gains and losses of carbon through flowing water, biomass removal, or methane fluxes). Pristine peatlands are considered to be net carbon sinks [3], but changing environmental conditions due to global warming or land use change may increase CO\({}_{2}\) release and potentially shift peatlands from being carbon sinks to sources [4; 5]. For that reason, it is important to estimate peatland CO\({}_{2}\) exchange across time and space. A common method to measure land-atmosphere CO\({}_{2}\) exchange at the ecosystem level is the eddy covariance (EC) technique [6]. The EC method provides direct measurements of NEE and nighttime ER, which can be subsequently partitioned into GPP and daytime ER. As the flux partitioning relies on modelling procedures, EC-derived GPP and ER are not considered to be direct measurements but modelled estimates at the ecosystem level. However, there is only a limited number of EC sites measuring peatland carbon exchange. Peatlands are widespread across the northern latitudes, and to enable the estimation of the CO\({}_{2}\) flux from all of these peatlands, it is important to develop remote sensing-based methods for upscaling their CO\({}_{2}\) fluxes. The estimation of peatland carbon fluxes with optical remote sensing presents a series of unique challenges due to several characteristic features of peatlands, such as the high proportion of non-vascular plants, high water content, and the effects of microtopographic variations [7]. As a result of their heterogeneity, carbon fluxes may vary widely between peatlands [8; 9] and within peatlands [10; 11]. Unlike CO\({}_{2}\) concentration, CO\({}_{2}\) fluxes are not directly detectable by satellites, and hence remote-sensing-derived estimates rely on the relationship between the fluxes and remotely sensed environmental variables. A widely used method to estimate GPP is the light use efficiency (LUE) model by [PERSON] [12], where GPP is the product of the total photosynthetically active radiation (PAR) incident on the vegetation, the fraction of photosynthetically active radiation absorbed by vegetation (fAPAR), and the conversion efficiency of the absorbed energy (\(\epsilon\)). Several studies have shown that fAPAR is linearly or near-linearly correlated with spectral vegetation indices like the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), and therefore a vegetation index often provides a proxy for fAPAR. Although NDVI is widely used in remote sensing-based ecosystem studies, EVI is less sensitive to perturbing factors like atmospheric effects (water vapor and aerosols) and soil reflectance, and more sensitive to variations in vegetation structure [13]. The two-band EVI (EVI2) has been developed to provide similar information as EVI, but it only uses the red and the near infrared (NIR) bands, whereas EVI also uses the blue band [14]. Despite the differences between the spectral indices, both NDVI and EVI have been successfully used to estimate GPP in peatland ecosystems [15; 16]. To obtain the full CO\({}_{2}\) balance of an ecosystem, both GPP and ER need to be estimated, but as [PERSON] et al.