## 1 Introduction
Remote sensing techniques provide a new opportunity to assess the C dynamics of peatlands and other remotely located ecosystems [23; 24]. It can facilitate an assessment of multiple ecosystem attributes and can provide information across wider spatial scales. This is important, as EC infrastructure is expensive, requires regular maintenance, and is limited to ecosystem scale perspectives, making direct measurements across multiple sites and spatial scales unfeasible. Because of this, the use of spectral indices as a proxy for carbon flux estimation are becoming more widely used [14; 25; 26]. Vegetation indices derived from satellite data, such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), have been correlated with GPP with varying degrees of success at grassland, cropland, and forested sites [14; 16; 27]. The use of common MODIS-derived vegetation indices (250-500 m), such as NDVI and EVI, can be challenging for GPP estimation over peatlands because of the narrow red absorption feature and narrow near-infrared reflectance peak which is commonly observed in dominant peat-forming species, such as _Sphagnum_ mosses [25; 28]. Additionally, vegetation indices derived from MODIS data might incorporate forestry and grasslands into the peatland signals due to the coarse spatial resolution (250 m-1000 m), leading to overestimation of GPP [29; 30]. However, the use of higher spatial resolution sensors, such as Sentinel satellite imagery (10-30 m), can help reduce the uncertainty in GPP estimation by providing greater insight into the carbon flux estimation of heterogenous landscapes [8]. The reflectance characteristics of peatlands are different from forest, agricultural, and grassland ecosystems due to the hydrological conditions, water content, and associated vegetation communities [31; 32]. Moreover, the spectral signature of _sphagnum_ mosses is characteristically different compared with the vascular vegetation in peatland ecosystems [13]. The spectral reflectance of _sphagnum_ species changes with the available moisture content, and their reflectance increases during dry periods as _sphagnum_ changes its color to yellow-brown [33; 34; 35; 36]. [PERSON] et al. [37] highlights the importance of using different indices within one model to properly understand the effect of water content on vegetation when using light use efficiency models. Therefore, there is a need to assess the performance of widely used indices to predict GPP and develop a combination of indices specifically for raised bog ecosystems. In this study, Sentinel-2 high-resolution satellite imagery was used to derive vegetation and water indices, as it offers a new perspective in monitoring ecosystem phenology [8; 14; 32]. Six widely used indices, which include four vegetation indices (NDVI, EVI, MCARI, and REP2) and two water indices (LSWI and NDWI2) were used in this study to identify the best combination of indices for peatlands. Sentinel-2 images from a climatologically dry year in 2018 were used to derive indices, as it provided an opportunity to acquire seasonal cloud-free imagery for GPP estimation, and it was also useful to gain further insight into how the light use efficiency parameters performed during drier conditions. The objectives of this study were to (1) develop hybrid models using a combination of indices to estimate GPP using LUE models and validate them against EC-derived GPP for a near-natural peatland ecosystem and (2) determine the site-specific light use efficiency parameters for a near-natural peatland under dry and normal conditions, which could be used to refine estimates of LUE-model-based GPP from other sites in the future.