## 5 Discussion
### Limitations and Way Forward  
This study detected the phenophases of the crops using an optimised, robust, and unified approach. However, it still has some inherent limitations related to the data and methods, which are discussed in the following sections and could be attributed to several factors. First, the study covers only selected phenophases because the DWD only observed individual phenophases of the respective crops, rather than the entire growth cycle. In addition, biased or incorrectly reported information from DWD could lead to inaccuracies in representing the correct phenophases. This issue is common in volunteered geoinformation data, which, despite being a good source of free and abundant data, can be influenced by biases caused by volunteer observers, such as a lack of field identification skills and geographic bias [65]. Specifically, ref. [66] found that 21% of the reported DWD phenological observations in Germany between 1959 and 2009 were questionable, with issues such as observations assigned to the wrong season or different phenological stages recorded on the same date for the same site and year. Therefore, adopting a new data filtering procedure could lead to filtering out these questionable observations and to increasing the accuracy of phenological observations [46; 67]. Another consideration is that, according to the DWD instructions, each observation associated with a DWD station is considered representative of the crops within a maximum distance of 5 km from that location. However, some studies, such as [68], argue that the observations are based on at most four fields in the best cases. This means that the entry dates of the selected phenological stations could differ significantly when considering multiple fields for comparison, potentially worsening the results. Second, another limitation of the present study worth highlighting is the accuracy of parcel geometries of the crop types derived from IACS data, which is generally considered an excellent reference source. However, it can still contain errors due to inaccuracies in digitisation or false claims, and some farmers might not apply for subsidies [69]. Consequently, some outlier profiles might be attributed to a different type of crop, leading to incorrect detection of phenological estimations [54]. Building on the identified limitations, several key areas for future work and the way forward are crucial to advancing our approach and improving its performance: First, the UE-WS \(NDVI\) approach proposed in this study has already shown high performance in reducing noise and reconstructing high-quality \(NDVI\) profiles for phenological analysis. However, this \(NDVI\) time series might still be challenging for phenology detection if clouds persist over a field for an extended period or the frequency density of satellites is very low. This situation provides an excellent opportunity for spatiotemporal fusion to provide more informative data and support the current phenological analysis when temporally sparse Sentinel-2 observations occur[70] as well as the possibility to reconstruct and predict \(NDVI\) of cloud-contaminated images by establishing a relationship between SAR and optical images [71]. Second, although \(NDVI\) has been effective in estimating various crop-specific phenophases, there is a need to explore alternative vegetation indices that could address NDVI's limitations, such as saturation. These alternative indices might offer more precise phenological information [40]. Third, while the study demonstrated strong regional and annual transferability of the thresholds obtained for most crops and phenophases within the available ground truth field measurements, limitations in reference data availability across different years and areas remain a bottleneck to performing a comprehensive assessment of this transferability, aiming to achieve high-precision estimates at the crop parcel level across Germany.