## 4 Discussion  
This study aimed to identify the best methodology for the estimation of phenological dates through satellite-processed vegetation indices. As in other studies [26; 27], GPP measurements were used to extract SOS, POS and EOS as observed data to be compared with those elaborated from kNDVI patterns. Due to the valuable relationship between kNDVI and GPP found for grasslands in [47], the use of this index enabled phenological dates (i.e., SOS and POS) to be estimated in agreement with those extracted by GPP patterns at sites endowed with eddy covariance flux towers. Various fitting models and extraction methods were tested and evaluated in our study, providing different results for SOS, POS and EOS detection. For consistency, a trend analysis was performed by deploying only one fitting model (i.e., ELM) among those selected. Then, we identified the best extraction method for each specific phenological date (i.e., TRS\({}_{0,3}\) and GU for SOS and POS, respectively). It is important to underline that the fitting model (i.e., the curve that fits kNDVI points and from which dates are extracted) is the same in all trend analyses (i.e., SOS and POS) and for all sites and years. The choice to use different extraction methods on the same curve for SOS and POS derived from the results obtained in Section 3.1. Although it was more coherent to select only one method, we decided to select the most robust extraction methods for each phenological date (i.e., SOS and POS) in order to achieve more precision in SOS and POS estimation during the analysis of the 2001-2021 period. In fact, using the same extraction method could determine greater errors, since, for example, one method can be optimal for SOS but sub-optimal for POS estimation. SOS and POS dates extracted from kNDVI were in line with those estimated from GPP, even if some uncertainties were still present, as seen from MAE (13.6 and 13.4 days, respectively for SOS and POS) and RMSE values (16.9 and 18.5 days, respectively for SOS and POS). EOS, conversely, proved to be more difficult to detect than SOS and POS. This is confirmed by [PERSON] et al. [15], who achieved lower levels of accuracy in estimating EOS in different environments, and by [PERSON] and [PERSON] [53], who observed large differences  
Figure 9: Example of relationships between SOS and the mean temperature of the 30 days after SOS at the DEN site. and poor correlation between EOS extracted from satellite vegetation indices and ground-observed EOS in a specific study on grasslands. Differently from [PERSON] et al. [15] and [PERSON] et al. [14] for forests and croplands, in this study the estimation of EOS from satellites did not reach a level of accuracy that can provide a reliable analysis for EOS trends over the period 2001-2021. However, it should also be noted that the extraction of SOS and EOS dates in grasslands is subject to greater uncertainties than in other environments, such as deciduous-broadleaf and mixed forests [54]. Leaf senescence responses in herbaceous species are influenced by several meteorological variables, with a complex dependence on species, functional types and geographical gradients [55]. Differences in scale and content (spectral response and phenological event) between satellite-derived and ground-observed phenology can result in discrepancies between satellite-derived phenological dates and changes in leaf colouring, although these measurements are related (e.g., EOS and the beginning of leaf colouring) [56; 57; 58]. This is especially noticeable for EOS. In fact, the change in canopy greenness is slower and longer in autumn with respect to spring [59; 60], thus causing a reduced variability in EOS compared to SOS and a greater difficulty in detecting the end of season from satellite [15; 53]. From our trend analysis, the European grasslands analysed showed a general advance in the start (SOS) and peak of the season (POS) over the 2001-2021 time period.