## 1 Introduction
Since their first use in the early 1970s, VIs have evolved rapidly together with the increased number of spectral bands of the sensors and the new demand for measuring specific ecological indicators [7]. Particularly, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) have been widely used for the monitoring of primary productivity. Empirical models linking the NDVI and EVI with GPP or NPP in situ observations have shown accurate NDVI-GPP correlation in low biomass vegetation areas [9; 22] and good performance of the EVI in high biomass vegetated areas such as dense grass or forest ecosystems [23; 24]. Additional VIs have also been investigated, such as the two-band Enhanced Vegetation Index (EVI2) used by [PERSON] et al. to assess the relationship between the EVI2, meteorological variables and GPP within the Nordic region [25]. A recently suggested vegetation index, the Near-Infrared Reflectance of terrestrial vegetation (NIRv) [26] has also shown a high correlation with GPP across different ecosystems [27; 28] and temporal scales [29; 30]. Recent outcomes in research suggest that given that GPP is not only connected to the greenest of vegetation, but also to other processes such as temperature or content water of leaves, different VIs sensitive to these factors need to be evaluated in carbon flux studies [31]. Moreover, since specific VIs are more sensitive to particular ecosystems, a broader analysis for the selection of a specific VI depending on the study region is required for a better model formulation [32; 33; 34]. Currently, remote sensing based global products of PP are provided by the National Aeronautics and Space Administration (NASA) through the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This product, known as a MOD17 product, provides estimates of GPP on an 8-days basis, aggregated to annual products to compute NPP, with a spatial resolution of 250 m, 500 m and 1 km. MOD17, designed to provide an accurate regular measure of terrestrial vegetation growth, has been available since the mid-2000s [35; 36; 37; 38]. Although the MOD17 product is widely utilized, its use and applicability in conservation and management practice are limited by the trade-off between its temporal resolution, spatial resolution, and spatial extent [35; 39; 40]. MOD17 coarse spatial resolution (1 km) does not provide sufficient relevant information to monitor processes at a fine-local scale and represents a critical factor for carbon flux estimation, especially in heterogeneous landscapes [41; 42]. Additionally, MOD17 GPP products are estimated by means of biome-specific parameters, parametrized and applied to biomes at a global scale [35]. While this simplification is suitable for global estimations of GPP, the coarse inputs and the use of global Biome Parameter Look-up Table (BPLUT) parameters reduce the capacity of MOD17 to describe ecologically significant variation at finer scales [35; 37; 43; 44]. Different authors have identified the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI) as a potential sensor capable to improve the estimation of PP at a local scale, given its higher spatial resolution (10 m, 20 m, and 60 m). [PERSON] et al. [20] listed several potential applications of Sentinel-2 sensors, including the generation of indicators of vegetation phenology, fire damage extent, defoliator control, habitat extent, habitat quality, production of biomass, etc. According to [PERSON] et al. [25], the Sentinel-2 data can better capture the spatial variation in heterogeneous landscapes in comparison to MOD17 products and enable the estimation of GPP at finer scales. [PERSON] et al. [33] have shown that the narrow red bands from Sentinel-2 sensors enhance the GPP estimations, increasing the accuracy of the empirical relationship between remote sensing based VIs and GPP which is influenced by the spectral resolution of the satellite products. To provide information on ecosystems and biodiversity status, this study builds on the methodology proposed by [PERSON] et al.