## 5 Discussion
### Phenological Estimates and Optimisation  
In this study, we proposed a full workflow for optimising the estimations of various crop phenophases using generated high-quality \(NDVI\) data. The process started with robust identification of the right phenological cycle of the corresponding crop field using the approach described in Section 3.2 and illustrated samples in Figure 4b. Once the phenological cycles have been correctly identified, the phenological metrics (EOS/SOS) estimation becomes possible by setting a certain threshold on the time series. However, as has been discussed above, there is no single threshold that could be applied to all crop types, but in practice, the SOS/EOS threshold will depend on the crop type and targeted phenophases. Although the importance of such parameterization is high, most studies use thresholds that are mostly determined empirically, and there are surprisingly only a few studies that have tried to optimise the thresholds for phenology extraction [62]. Specifically, ref. [36] investigate the optimal thresholds for detecting crop phenophases by evaluating the retrieval accuracy of the SOS and EOS using different dynamic thresholds of VIs time series from MODIS data by steps of 5% from 0% to 35% for each of the crops. The same range of threshold values was tested in another study by [40] but on a finer scale using S2 phenology data. However, this approach is generally based on the estimation of vegetation phenology without focusing on specific crops. This contrasts with a study conducted in Germany [38] that obtained good accuracy in calibrating local \(NDVI\) thresholds from 0 to 1 with a step size of 0.01, but this was performed for only three crops and two phenostages per crop type with limited ground-truth data. Regarding all this, we propose a new optimisation framework that combines extensive ground-truth data from DWD phenology observation and the high-quality produced \(NDVI\) data to calibrate the optimal thresholds using the Optuna framework in two steps from 0 to 1 for 14 phenostages and from three crops, as discussed in Section 3.2.3 and seen in Figure 3. Once the optimised thresholds have been derived, the assessment of satellite-based phenometrics using these optimized thresholds will be applied using two sources of ground-truth-observed phenology records: (1) a separate year of phenological DWD observations and (2) BBCH records from several ground-truth field measurements samples. In general, we found good agreement between satellite-based phenometrics and ground-truth observations for almost all the crop phenophases investigated (Figures 6 and 7). Given the differences in the study area, period, and methods used to compute crop phenophases and due to the scarcity of studies published to optimise \(NDVI\) thresholds, a direct comparison with other studies is not straightforward. However, some comparisons may be of value. In this context, our optimised thresholds seem to be consistent with some previous studies. The authors of [35] compare the phenology from the radar cross-polarization ratio VH/VV and from S2 \(NDVI\) with ground-truth phenological observations from DWD using three LSP metrics: peak season occurring when the function reaches 90% of the green-up amplitude (PS90), SOS extracted using the 50% thresholds (SOS50), and EOS extracted using the 50% thresholds (EOS50). Although the main objective of the study was not to compare several thresholds, it was found that in most cases, SOS50 was correctly associated with the early stages of BBCH (shooting, leaf development, and stem elongation). Except for wheat, where PS90 yields good results in predicting the BBCH stage (35, shooting), this would be close to the threshold we found with the optimisation in Figure 5 (90%). The study also found that the late secondary stages of the ripening stage for winter wheat and the harvest stage for corn were the closest stages to the EOS50 timing. This is in agreement with our findings, where our optimised thresholds vary around 50%. Ref.