## 1 Introduction  
Northern peatlands have played a significant role in carbon (C) accumulation for millennia [1]. Because of their large extent in northern regions (e.g., 12% of Canada, or 1 million km\({}^{2}\)) [2; 3; 4], it is important to evaluate their potential response to climate change [5; 6]. Remote sensing studies of peatlands from both hyperspectral airborne and multispectral satellite sensors have shown potential in providing fundamental information about vegetation (e.g., leaf area index) and hydrology, especially with respect to the spatial patterns and temporal trends of these characteristics at large spatial scales (>100 ha) (e.g., [7; 8; 9; 10; 11]). The advantage of airborne hyperspectral imagery in peatland research is that it can be collected at a fine spatial resolution (\(\leq\)1 m) to carry out detailed mapping and modeling studies (e.g., [8; 9; 12]). However, due to the inaccessible nature of many peatlands [3] and the elevated cost of such data acquisition, especially for isolated northern peatlands, satellite imagery might be the most feasible long-term monitoring solution for these ecosystems. A novel aspect of peatland research using remotely sensed data is the integration of airborne hyperspectral imagery with multispectral satellite data for ecosystem modeling (e.g., hydrology, biogeochemistry). Optical imagery in the VNIR (visible-near infrared) and SWIR (shortwave infrared) ranges (400-2500 nm) has been used to determine spectral differences between mosses and vascular plants in peatlands [13]. Spectral indices incorporating both narrow and broadband sensors (e.g., [14]) successfully assessed the relationship between surface moisture and water table position experimentally and at small spatial scales [8; 15]. However, remote sensing studies at the ecosystem level are still needed to evaluate the full potential of such imagery, especially for peatlands where in situ data are available. Furthermore, a more thorough understanding of the effects of illumination and viewing geometries are needed for studies considering seasonal changes. The difference between the carbon dioxide uptake through gross primary production (GPP) and ecosystem respiration represents the net ecosystem exchange (NEE) [16]. In situ measurement of NEE via approaches such as flux chambers can be utilized to assess responses to diverse variables, including nutrient inputs (e.g., [17]) and manipulations of water content [18]. Eddy covariance towers are also commonly used in the assessment of NEE in peatland ecosystems, with the advantage that these provide a larger footprint than chambers and can provide near-continuous long-term data [19; 20]. For large-scale studies, MODIS-derived products have been used to assess NEE [21; 22], however, the large spatial resolution (e.g., 0.5-1 km) is inadequate to capture the small-scale heterogeneity of peatlands, along with other known limitations for peatlands such as the errors in the vapor pressure deficit determined from land cover (for GPP estimation) [23; 24]. More recently, the potential of Landsat 7 ETM+ data (30 m resolution) was shown for determining peatland classes with high accuracy [23], from which to estimate the C balance. Given the long history of the Landsat program, a less explored aspect is the development of C models (e.g., NEE) taking advantage of these multitemporal data. In addition, new satellites such as Sentinel-2 provide additional opportunities to evaluate Landsat products at finer spatial scales (e.g., 10-20 m spatial resolution), which may better capture the spatial heterogeneity of these ecosystems. Biogeochemical processes such as gas exchange (e.g., CO\({}_{2}\), CH\({}_{4}\)) and C accumulation in northern peatlands are closely related to the water table position [25; 26; 27]. Given a lowering of the water table, an increase in CO\({}_{2}\) loss is expected [25]. For example, the interannual variability of C exchange at the Mer Bleue peatland indicated that this ecosystem reduced its CO\({}_{2}\) sink capacity during summertime drought events [20].