# Estimation of Gross Primary Productivity (GPP)
## 4 Discussion
### Quality of Satellite Raw Data, Filtering and Threshold Methods  
The filtering methods HANTS and Polyfit reproduced a better method for the seasonal and interannual variability of GPP of the SRC plantation (Figure 3; Table 3) than the SavGol method.  
Furthermore, both filtering methods were suitable for identifying the start and the end of carbon flux uptake (Table 4), as reported in previous studies [124]. [PERSON] et al. (2012) [125] reported that fitting methods where each parameter of the fitting function is estimated by the least-square method are more robust. However, there is not a unique method that always performs better than another for filtering VI time-series [64].  
The differences between the different filtering methods in reconstructing VI time-series could be related to their ability in detecting clouds and cloud shadow. The presence of clouds changes the optical properties of the signal. The cloud shadow weakens the light intensity and thus the reflectance of all bands is underestimated [64]. Furthermore, while the presence of clouds causes an underestimation of VIs, cloud shadow can lead to an overestimation of NDVI [64]. A limited description of the EOS in all years by most of the VIs could be also related to the large presence of clouds at the end of the summer and in autumn when senescence started (Figure 3, Table 4).  
Another point worth mentioning relates to the accuracy of cloud masks used in the screening of clouds and cloud shadows. In this study, we used postprocessed Sentinel-2 data by Sen2 Cor algorithm. The cloud classification generated by Sen2 Cor shows limits in accurately identifying the cloud shadows. This classification does not reduce the noise of the time-series data but it produces a misleading classification of the satellite data quality. Therefore the remaining data with low quality but classified as good pixels might affect the patterns of fitted VI time-series (Figure 3, Table 3). The number of high-quality data of the time-series and their distribution in time affects the accuracy of the fitting functions [63]. A comparison of filtering methods is advisable, given their contrasting performance in the literature [64; 89; 125].  
In addition to the quality of VI time-series and the robustness of the filtering functions, the accuracy of the phenophases also depends on the chosen detection method [126], i.e., the selected dates of SOS- and EOS occurrence depend on the thresholds that are chosen to represent them (Figures 4 and 5). In this study, we found that for SOS, the dates at which the 10% and 20% percentile occurred showed very good agreement between VIs and the GPP data, while for EOS the dates of the 10% percentile came out as the best threshold (Table 5). The percentile method focuses on the position of the proxy for a fixed percentage (e.g., 10% and 20%). Several previous studies also obtained good results using such low percentiles as thresholds for determining the dates of SOS and EOS [127; 128; 129; 130; 131]. Furthermore, a threshold around 20% is good for estimating the start of greening [132]. Our analysis confirmed also that the extraction of phenological metrics from VI time-series is highly uncertain [65; 66; 67]. Combining multiple methods is preferred especially for estimating EOS dynamics (Table 5). The first derivative method is very sensitive to the noise in the signal and the temporal filtering and it cannot represent short growing seasons well, especially when the increase and decrease in the annual time-series occur rapidly and abruptly [133]. The percentage method is sensitive to the minimum and maximum values that may be affected by noise in the signal.