## 6 Conclusions  
Although many approaches have been proposed to retrospectively capture and map phenological phases, the calibration and optimisation of these phenophases that are specific to certain crop types and re-establishing their definition to be linked with specific agronomic stages of interest have rarely been addressed. This is mostly because it remains challenging to implement an optimised, robust, and unified approach to detect crop-specific phenophases on a regional scale from an optical remote sensing perspective in cloudy and rainy areas. In this context. reducing noise and reconstructing high-quality \(NDVI\) profiles for further time series analysis is a promising and effective approach to more reliably predict accurate crop phenophases. Using ARD Sentinel-2 time series as the core of this study, we proposed a compound approach based on envelope detection and a smoothing filter to robustly produce high-quality \(NDVI\) data and identify individual valid vegetation cycles before the phenological retrieval. The results revealed that the new method is effective for reconstructing high-quality \(NDVI\) data to represent consistent phenological profiles of the different crops studied. Moreover, these high-quality \(NDVI\) time series data, along with extensive field information from collected in situ data and DWD phenology observations, were used together to calibrate and optimise the thresholds for extracting crop-specific land surface phenology. Our optimised thresholds provided enhanced capability in accurately estimating phenophases and achieved satisfactory accuracy that links more directly to in situ phenological observations. Moreover, the thresholds were used to predict field phenological information and thus illustrate more detailed spatial and temporal information. This study provides an advanced cloud platform-based phenology study case under different scenarios at landscape levels and provides technical support and application demonstration for field-level research, such as simulations of crop growth models and the analysis of the underlying reason for yield gaps at the field level.  
Conceptualization, A.H., M.M. and H.G.; methodology, A.H., M.M. and H.G.; software, A.H.; validation, A.H., T.R.and H.G.; formal analysis, A.H.; investigation, A.H.; data curation, F.B., T.R. and H.G.; writing--original draft preparation, A.H.; writing--review and editing, T.R., M.M. and H.G.; visualization, A.H.; supervision, H.G.; project administration, H.G.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
This research has received funding from the German Federal Ministry of Economic Affairs and Climate Action as part of the NaLamKI project under grant number 01 MK21003E.  
Due to the sensitive nature and related data privacy regulations regarding phenological in-situ data from PIAF database is confidential and cannot be shared. Restrictions further apply to the availability of phenological in-situ data from patchCrop experimental site. Requests to access this data should be directed to [PERSON] from Leibniz Centre for Agricultural Landscape Research. All other data used in this study will be made available upon request.  
We are very thankful to the Leibniz Centre for Agricultural Landscape Research (ZALF) for sharing data from the patchCROP experiment with us. The maintenance of the patchCrop infrastructure is supported by ZALF. We further thank the plant protection services of the German federal state for the provision of phenological data for validation of our results. We finally thank the operators of CODE-DE for providing a tailored EO cloud computing platform for public organisations.  
The authors declare no conflicts of interest.