## 4 Discussion
Ideally, a smaller time window during the period with high productivity, when the VI selected for the model formulation is rapidly changing, should yield higher model accuracy and abides by the prerequisite of stable VI. At the same time, enlarging the time window could contribute to a reduction in the contamination and noises in the images, and therefore, lead to a smoother GPP estimation [85]. We, therefore, suggest further investigation of the model development when upscaling it. Including more specific land cover classes to account for different vegetation types or ecosystems [86; 87; 88] or applying daily instead of annual climatological footprint [25; 89] can also allow better estimations of GPP and should be pursued in further research. To perform the classification, we used the k-means clustering algorithm [70]. The algorithm was quick and easy to run, a main advantage in comparison with supervised classification, which would have required extensive prior knowledge of the area to be able to identify and label the classes for the training data set [90]. Supervised classification algorithms, however, can also be considered to further improve the land classification of the study area. Supervised classification methods are in general more expensive since require more time and prior knowledge of the area, but can perform better if a good quality training data set is available [91]. The heterogeneity of the area surrounding the EC tower and the climatological footprint represents a critical factor for carbon flux estimation from satellite imagery [41]. High-resolution products that accurately describe the observed patterns and processes occurring at different temporal and spatial scales are needed [35]. We demonstrated that the high spatial resolution of the Sentinel-2 products allowed a detailed description at the time of the distribution of GPP over the ecosystem, enhancing the MOD17 coarse 500-m resolution GPP products. We conclude, therefore, that Sentinel-2 multispectral and high-resolution products can enhance the evaluation of ecosystem responses at a fine scale. The model versatility is ensured by the integration and assessment of multiple remotely sensed indexes with different environmental variables. This can facilitate the uptake of the workflow in different ecosystems. The advantage of assessing multiple indexes is justified by the fact that the sensitivity to specific bands combination depends on the characteristics of the vegetation or climatic conditions [32; 33]. A clear example of this is the saturation of NDVI in high biomass vegetation areas and the use of EVI instead [92]. Regardless of the development of multiple vegetation indexes formulated to characterize specific vegetation features and processes in different regions, many studies on primary productivity still rely on the use of NDVI or EVI [25; 41; 45; 92; 46]. It can be assumed that those VIs will have sensitivity to any ecosystem component or process. Nevertheless, ecosystem functioning processes such as primary productivity could be explained by multiple remotely sensed information and not exclusively by the fraction of absorbed photosynthetically active radiation (fAPAR), commonly approximated with the NDVI and EVI. An additional concern about the use of those indexes is that multiple studies have derived models for Nordic ecosystems [25; 45]. Regardless of the accuracy derived for those ecosystems, upscaling the approach to other regions would require further analysis. For instance, the assessment of drier ecosystems for which the water or temperature availability can play an essential role rather than the light availability or fAPAR would require the use of VIs that reflect these limiting factors. Examples of these indices are the water-sensitive VIs such as the Land Surface Water Index (LSWI) and the Modified Normalized Difference Water Index (MNDWI), which are more sensitive to droughts [34] or the Green Normalized Difference Vegetation Index (GNDVI) which is more sensitive to chlorophyll and so to photosynthesis activity [32].