## 1 Introduction  
Peatlands are vital ecosystems in the global carbon (C) cycle, as they act as a significant store of carbon [1], with estimates of soil organic carbon (SOC) stocks of between 500 to 700 billion tons of C globally [2]. However, these ecosystems are prone to degradation, primarily through drainage to facilitate a change in land use, which results in a release of approximately 2-3 gigatons of carbon dioxide (CO\({}_{2}\)) to the atmosphere every year [3]. To better understand how we can rehabilitate these systems and enhance their climate mitigation potential, it is important to quantify their C dynamics and the key drivers of C uptake and release at various spatial and temporal scales. Natural and managed peatlands in Ireland are estimated to store up to 2320 Mt of C on \(\sim\)20% of the land area [4, 5]. Irish peatlands have been extensively drained over the past several decades for extraction for both energy and horticulture and for conversion to agriculture and forestry [6]. These land-use conversions have significantly affected the greenhouse gas (GHG) dynamics of these ecosystems, and they are estimated to release approximately 11-12.4 Mt CO\({}_{2}\) per year [7]. Furthermore, the conservation of near-natural peatlands and the rehabilitation of degraded peatlands is necessary to protect C stocks, reduce emissions, and enhance their C assimilation capacity to further offset national GHG emissions. Terrestrial gross primary productivity (GPP) describes the total amount of CO\({}_{2}\) assimilated by plants in an ecosystem through photosynthesis and is considered the largest flux component of the global carbon cycle [8]. Therefore, detailed knowledge of the spatio-temporal extent of GPP dynamics and the key drivers, such as air temperature, water table depth, and soil water content, is imperative to improve our ability to quantify fluxes and upscale them across various spatial domains. Current field-based methods of measuring annual carbon exchange between peatlands and the atmosphere include the use of static and dynamic chambers and eddy covariance (EC) towers [9; 10]. The integration of both these techniques can encapsulate peatland carbon dynamics at a variety of temporal and spatial scales by capturing seasonal and interannual variability at the point-source and ecosystem scale. While EC techniques have proven to be of great importance in the measurement and modeling of C dynamics in peatlands [11], these measurements account for C fluxes within the footprint of the tower only, which typically spans over an area between 200 m\({}^{2}\) to 1 km\({}^{2}\). It is, therefore, challenging to scale up EC fluxes from the footprint-derived source to the regional/global scale due to the geographical distribution and limited number of towers [12]. Light use efficiency (LUE) models based on [PERSON]'s theory (1972) are extensively used to model GPP. This model expresses GPP as the product of the incident photosynthetically active radiation (PAR), the fraction of absorbed photosynthetically active radiation (f\({}_{\text{PAR}}\)) by the vegetation canopy, and the light use efficiency parameter (\(\varepsilon\)). Spectral indices are important, as they integrate nutrient and absorption characteristics and are used as a proxy for f\({}_{\text{PAR}}\), which is one of the major parameters for the estimation of GPP in the LUE models [13; 14]. Rates of CO\({}_{2}\) assimilated by a plant canopy have shown to be strongly correlated with satellite-derived vegetation indices [15; 16]. The light use efficiency parameter (\(\varepsilon\)) can also vary depending on environmental conditions and plant physio-morphological changes [17]. It is also possible to calculate \(\varepsilon\) directly from the photo-chemical reflectance index (PRI); however, the bands required for PRI index are not available in Sentinel 2 data [16; 18; 19]. There are also a limited number of studies available that report the derivation of \(\varepsilon\) values for peatlands using site-derived light response curves (LRC) [20; 21; 22].