# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 1 Introduction
[7] highlight, remote sensing-derived peatland studies have mainly focused on estimating GPP with less consideration of ER. The key challenges are to estimate heterotrophic respiration with high accuracy using remote sensing data [17] and to explain the variability in ER across ecosystems [18]. Despite the challenges, ER has been successfully modelled using satellite land surface temperature (LST) [16; 19], and some studies have also included vegetation productivity and soil moisture to improve the ER estimates [18; 20; 21; 22]. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument mounted on NASA's Terra and Aqua satellites is widely used to monitor vegetation dynamics, land cover changes, and LST at local and global scales, as it provides both optical and thermal data at a high temporal resolution (1-2 days). However, the heterogeneous character of peatland ecosystems presents a challenge for remote sensing using coarse-resolution (250 m-1 km) sensors like MODIS [23; 24]. The MODIS GPP/NPP (MOD17) product at 1 km spatial resolution provides GPP estimates in different biomes [25]. However, the MOD17 product lacks a peatland category in its land cover classification and an estimate of surface moisture, which limits its applicability in peatlands. The possibility to generate time-series data at high spatial (10-60 m) and temporal resolution (2-3 days at mid-latitudes) using the new Sentinel-2A and 2B satellites with the MultiSpectral Instrument (MSI) presents a new opportunity to map CO\({}_{2}\) fluxes in these landscapes. Nevertheless, even with the high frequency of data provided, the presence of clouds and seasonal snow cover cause data gaps of varying length. The TIMESAT v.4 software package provides a flexible yet robust data processing method based on box constrained double logistic functions combined with splines to gap-fill and smooth time series data [26]. The availability of high spatial resolution satellite data also raises the question of how to select a spatial sample of the data that is representative of the EC flux footprint area. Accounting for the flux footprint may be particularly important in peatlands due to their spatial heterogeneity, and can be achieved by using footprint models that can help select the satellite pixels that fall within the EC footprint area [27; 28; 29]. The potential of remote sensing-based models to predict peatland carbon fluxes has been demonstrated by [PERSON] et al. [16]. We will expand on this work by using newly available high spatial resolution (10-20 m) optical Sentinel-2 MSI data at five peatland sites across Sweden and Finland, state-of-the-art time-series processing, and footprint modeling to characterize the source area of the CO\({}_{2}\) fluxes. Although other carbon fluxes, such as methane (CH\({}_{4}\)) emissions or dissolved carbon exported by water, also play an important role in peatlands, this study focuses on CO\({}_{2}\) fluxes, as these are typically the largest component of carbon exchange in peatlands at annual timescales [5; 30]. The aim of this paper is to investigate the feasibility of developing a simple model based on remote sensing data to estimate the CO\({}_{2}\) balance (NEE) and its component fluxes (GPP and ER) across different peatlands types at northern latitudes, which could be used for upscaling CO\({}_{2}\) fluxes across northern boreal peatlands. As part of this aim, we investigate a) with what accuracy we can model the NEE flux and its components using a single parameter set for our five sites, compared to using individual parameter sets for each site, and b) with what accuracy directly modeled NEE differs from the sum of modelled GPP and ER.