## 5 Conclusions  
Cloud-free optical imagery is usually scarce over cloud-prone agroecosystems, implying that crop monitoring is often limited to a few observations during key phenological development stages. The synergy of optical with complementary all-weather SAR data can provide valuable insights into specific agricultural areas, even in the presence of persistent cloud cover. In our study, we demonstrated that fusing time series of S1 SAR and S2 optical data through MOGP proved to be a powerful and innovative methodology to reconstruct cloud-free time series data streams over the entire growing season. Pursuing the development of a powerful fusion approach that learns the dependencies of S1 & S2 time series data, we applied a processing framework for selecting the best MOGP model. The S1 & S2 MOGP CONV model was validated with relatively high accuracy against S2 GP VWC retrieved data over two winter wheat fields belonging to the BVCR 2020 and 2021 crop campaigns (wheat 2020: \(NRMSE_{(S2\&S1,orbits68\&141)}=10.1\%\)). The CONV models were afterward applied iteratively at the pixel level to reconstruct removed S2 acquisitions. The reconstructed VWC maps in the absence of optical data showed spatiotemporal consistency and made it possible to accurately capture complete phenology curves. We conclude that the cross-correlation of the time series of S1 and S2 data can be exploited for the purpose of cloud-free large-scale reconstructing of optical-imagery-derived crop trait data streams. Further research must be conducted to deploy the MOGP strategy over other croplands and target other vegetation traits while keeping the runtime within acceptable limits. [PERSON], [PERSON] and [PERSON] proposed the general objectives and goals of the research; [PERSON], [PERSON], [PERSON] and [PERSON] designed the field campaigns and collected the in situ data; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data and obtained the results; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] reviewed the paper and supervised all the procedures. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project (grant agreement 755617) (K.B.) and Ramon y Cajal Contract (Spanish Ministry of Science, Innovation and Universities) (J.V.). Not applicable. We would like to thank the Hilario Ascasusbi Experimental Station (HAEE) of Argentina's National Institute of Agricultural Technology (INTA) for the in situ database used in this study. This research has been held out by the Fundacion Carolina's 2021 doctoral program scholarship. This publication is also the result of the project implementation: \"Scientific support of climate change adaptation in agriculture and mitigation of soil degradation\" (ITMS2014+313011W580) supported by the Integrated Infrastructure Operational Programme funded by the ERDF. The research was also supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu (accessed on 5 February 2023)). The authors declare no conflict of interest.