## 4 Conclusions  
The pairing of Sentinel-1 and Sentinel-2 data and the use of their complementary information has considerable potential in a wide range of applications, including land cover classification, crop growth monitoring and deforestation mapping. However, the generation of reliable multi-modal and multi-temporal ARD from low-level Sentinel products remains a challenge. Here, a flexible, user-centric framework is introduced for the on-demand selection, download and processing of Sentinel-1 and Sentinel-2 data for a user-defined ROI and time window. The tool can be configured to meet specific user requirements, enabling rapid access to combined ARD for downstream applications with global coverage. The framework is built in Python and containerised using _Docker_ to ensure a consistent environment treating all necessary dependencies.  
The framework selects the minimum number of satellite tiles required to cover a particular ROI within a specific timeframe, optimising the time-consuming process of downloading Sentinel products. Furthermore, when multi-source data are required, the framework follows a standard pipeline for processing Sentinel-1--radiometric calibration, speckle filtering, terrain correction--and Sentinel-2--re-sampling--products, the subsequent collocation of these products and their clipping to the shape of the ROI. Additionally, challenges inherent to Sentinel data, such as the presence of \"no-data\" regions in Sentinel-1 products and a more appropriate selection of Sentinel-2 products based on local cloud masks instead of tile-level cloud percentages, are addressed. The ability to generate time series of collocated Sentinel-1 and Sentinel-2 ARD is demonstrated, providing further insight to users through time-dependent trends relevant to specific applications, e.g., crop growth monitoring.  
The current version of the framework is applicable to Sentinel-1 GRD products that provide radar backscatter amplitude; however, it could be extended to include Sentinel-1 Level-1 Single Look Complex (SLC) products; the latter's intrinsic phase information can be useful in applications such as land cover mapping and crop monitoring, which rely on interferometric coherence to detect changes in the SAR signal. Moreover, the framework  
Figure 13: Example of Sentinel-1 and Sentinel-2 multi-temporal attributes from the 29 May 2020 to the 12 August 2020 for a field growing peas in Scotland. (**a**) Mean NDVI trend for Sentinel-2 for the cloud-free acquisitions along with examples of RGB images and NDVI maps. (**b**) Mean of Sentinel-1 VV and VH polarisation normalised radar cross-section (RCS) along with examples of Sentinel-1 false colour composite images.  
could be extended to include data from other Sentinel missions, providing a wider variety of EO ARD, such as Sentinel-3 data relevant to marine observation and land monitoring, and Sentinel-4 and Sentinel-5 data enabling the potential to monitor air quality.  
Conceptualization, C.T., J.C. and I.A.; methodology, P.U., M.C. and C.D.; software, P.U., C.D. and N.P.; validation, J.C., N.P. and R.A.; investigation, P.U. and M.C.; data curation, P.U., M.C. and C.D.; writing--original draft preparation, P.U., M.C., C.D. and J.C.; writing--review and editing, M.M., I.A., C.M., R.A., K.N. and C.T.; visualization, P.U. and C.D.; supervision, J.C., M.M., I.A., C.M., R.A. and C.T.; project administration, I.A., C.M. and C.T.; funding acquisition, I.A., C.M., R.A. and C.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Union Horizon 2020 research and innovation programme, grant number 825355.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.