# Estimation of Gross Primary Productivity (GPP)
## Results
### Describing SOS and EOS Using VIs  
The three filtering methods applied to the raw data time-series produced different seasonal patterns (Figure 3). For GPP, the filters HANTS and SavGol described well the GPP pattern at SOS and EOS, while Polyfit did not perform well at EOS (Figure 3a). Furthermore, the HANTS filter captured best the GPP amplitude, and the SavGol filter followed best the interannual variability in GPP (Figure 3a). However, the estimates of phenological metrics of GPP showed a very small variability between years: day of the year (doy) 115 (SD \(\pm\) 6) for SOS and day 313 (SD \(\pm\) 8) for EOS. Figure 3: _Cont_. Figure 2: Seasonal variation of daily sums of Gross Primary Productivity (Daily GPP) and meteorological variables for three growing seasons at the poplar plantation in Lochristi. **(a)** Daily GPP in gC m\({}^{-2}\) d\({}^{-1}\); **(b)** soil water content (SWC in m\({}^{3}\) m\({}^{-3}\), black dots) and air temperature (green dots); and **(c)** global radiation (Rg in W m\({}^{-2}\) d\({}^{-1}\)). The coppicing in 2017 delayed the SOS of about ten days compared to 2018 (Figure 3a; day 118 (SD \(\pm\) 3) for 2016 and day 108 (SD \(\pm\) 1) for 2018). Very similar SOS dates were obtained for 2016 and 2017: day 118 (SD \(\pm\) 3) and day 120 (SD \(\pm\) 2) for 2017, respectively. The late start of the growing season in 2016 compared to 2018 related to the lower temperatures in spring (Figure 2b). The estimates of the EOS showed about a one-week difference between the 2016 (day 320 SD \(\pm\) 1) and 2017 (day 313 SD \(\pm\) 3) growing seasons and two weeks between the 2016 and 2018 growing seasons (day 305 SD \(\pm\) 5). The anticipated senescence in 2018 was linked to the low SWC in summer until the end of season (Figure 2b). The SWC recharge after the summer in 2017 prolonged the growing season in that year compared to 2018 (Figure 2b). For VIs, the three filtering methods showed similar good performance in reproducing seasonal and interannual variability of GPP time-series at SOS (Table 3). The accuracy varied largely among VIs (Table 3, RMSE = 0.09--14.33 umol m\({}^{-2}\) s\({}^{-1}\)). Better performance in predicting GPP pattern at the SOS than at the EOS (Table 3, RMSE = 0.09--5.65 umol m\({}^{-2}\) s\({}^{-1}\) for SOS and, RMSE = 1.12--14.33 umol m\({}^{-2}\) s\({}^{-1}\) for EOS) was found. MTCI provided the best results in describing GPP phenology in 2016, ChlRedEdge in 2017 and MCARI predicted GPP well in 2018. Higher values of RMSE were found at EOS, except for MTCI in 2016 and in 2018 and EVI in 2017. Overall, HANTS and SavGol filters describe the GPP seasonality better than Polyfit at SOS and EOS, while HANTS and Polyfit performed slightly better at EOS. Figure 3: Time-series of the seasonal variation of midday Gross Primary Productivity (GPP) and vegetation indices derived from Sentinel-2 images. (**a**) GPP (in μmol m\({}^{-2}\) s\({}^{-1}\)); (**b**) NDVI—Normalized Difference Vegetation Index; (**c**) EVI—Enhanced Vegetation Index; (**d**) MTCI—MERIS Terrestrial Chlorophyll Index; and (**e**) ChlRedEdge—Chlorophyll red-edge; (**f**) MCARI—Modified Chlorophyll Absorption Ratio Index; (**g**) PSSR—Pigment Specific Simple Ratio; (**h**) SIPI—Structure Insensitive Pigment Index; (**i**) gNDVI—Green Normalized Difference Vegetation Index. Blue circles represent original data; dotted black lines represent HANTS series fitted data; green lines Savitzky–Golay (SavGol) series fitted data; and dashed red lines polynomial (Polyfit) series fitted data. Up-pointing pink and down-pointing yellow triangles represent mean values, in days, of the start and the end of season, respectively, derived from filtering methods. Blue and orange lines indicate the start and the end of season of GPP, respectively. Scatter plots in Figure 4 shows the comparison between SOS derived from VIs and from GPP, grouped by year for threshold and filtering methods.