LUE models usually rely on biome-specific maximum LUE factors that are reduced due to environmental constraints such as air temperature and water content [21; 22; 23]. However, meteorological data and a biome-specific maximum LUE factor might be too coarse to accurately describe spatial and temporal variations of the LUE factor [11; 22; 24]. Due to this issue, empirical models based on remotely sensed input have been developed for estimating terrestrial GPP [25; 26; 27]. In these models the regression parameter represents the LUE factor since it scales fAPAR to the level of carbon assimilation. The simplest empirical models use only a vegetation greenness index such as the Normalized Difference Vegetation Index (NDVI) proposed by [PERSON] [28] or the Enhanced Vegetation Index (EVI) proposed by [PERSON] et al. [20] to estimate GPP. In comparison to the widely used NDVI, EVI is more adjustable for reducing soil and atmospheric effects and more sensitive to structural variations in vegetation [20]. The further developed two-band Enhanced Vegetation Index (EVI2) provides similar information on vegetation properties to EVI but only uses the red and near-infrared bands, whereas EVI also requires the blue band [29]. However, [PERSON] et al. [24] showed that a model based entirely on EVI only poorly explains the seasonal variation of GPP at sites with evergreen vegetation that experience summer drought. Especially in boreal forests the air temperature (T\({}_{\text{a}}\)) and the availability of light (PAR) regulate the seasonal photosynthetic activity [30]. In addition, summer drought is typically the result of high T\({}_{\text{a}}\) and increased vapor pressure deficit (VPD) [31]. Consequently, including PAR, T\({}_{\text{a}}\) and VPD variables in the model along with a remotely sensed vegetation index is assumed to improve the GPP estimation. When calibrating remote sensing models against EC CO\({}_{2}\) data, it should be noted that the EC data represent large exchange areas, roughly covering a distance of 10 to 100 times the height of the measurements over displacement height [32; 33]. The area influencing the measured flux is called the source area [34], the contribution of each part to the measured flux (source weight) is called the flux footprint, and it depends on wind direction, wind speed and turbulence, leading to the variation of footprint from one half-an-hour flux averaging period to another [35]. The rough approximation above disregards this variation in flux footprint, thus introducing scale and weather-dependent approximation errors [36; 37]. For accurate spatial representation of flux variations, a footprint model should be used [38; 39]. This type of model can be used to assess which pixels belong to the footprint and hence should be selected for the calibration. To model GPP on local and global scales, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board NASA's Terra and Aqua satellites have been widely used. Due to its high temporal resolution, MODIS provides data for monitoring vegetation dynamics and changes in land cover over time. However, MODIS acquires data at three relatively low spatial resolutions: 250 m, 500 m, and 1 km. The widely used MODIS GPP/NPP (MOD17, collection 6) product at 500 m spatial resolution has been questioned by many, mainly due to a lack of sufficient flux measurements for validation and calibration of the algorithm [40; 41; 42]. The spatial resolution of satellite products has been identified as a critical factor that limits spatially detailed carbon flux estimates, especially in heterogeneous landscapes [43]. The new Sentinel-2A and 2B satellites with the MultiSpectral Instrument (MSI) have great potential for studying CO\({}_{2}\) fluxes in terrestrial ecosystems due to improved spatial resolution (10 m, 20 m, and 60 m) and high temporal resolution (2-5 days' revisit frequency depending on latitude). A few studies have explored the performance of the Sentinel-2 products for CO\({}_{2}\) flux modelling, e.g., [PERSON] et al.