## 3 Results
### Spatiotemporal Mapping of Reconstructed VWC Based on S1 & S2 Synergy  
The S1 & S2 temporal trends presented in Figures 3 and 4 and the best MOGP model selected in Section 3.2.2 were used to demonstrate the feasibility of VWC maps reconstruction based on the S1 & S2 synergy. To accomplish this, we trained an independent CONV model per pixel over two subsets of the BVCR study site. The first subset (red dotted rectangle in Figure 1) contains three winter wheat paddocks that belong to the 2020 crop campaign and its surface is 56 hectares (71 \(\times\) 79 pixels at 10 m). The second subset  
Figure 7: CONV posterior predicted mean for a three-channel model. Red dots indicate the original observations for S2 GP VWC (CH-1), S1 RVI orbit 68 (CH-2), and S1 RVI orbit 141 (CH-3) while the green ones are the latent samples. Winter wheat 2020 ROI-1 (**a**). Winter wheat 2020 ROI-2 (**b**). (orange dotted rectangle in Figure 1) consisted of a large homogeneous parcel of winter wheat from the crop campaign 2021 located not far from the first, its size is 58.5 hectares (76 \(\times\) 77 pixels at 10 m). For each pixel in the S1 & S2 dataset, a CONV model was initialized with the parameters described in Section 2.5 and then trained using 500 iterations. It is worth pointing out that both spatial subsets selected for the application of the MOGP regression, have had the same crop rotation and management according to the agronomic practices of the HAEE. Winter wheat irrigation has been conducted during three distinct vegetative stages along the phenological cycle. While it is true that we applied the CONV model to the whole S1 & S2 dataset for both spatial subsets reconstructing 44 S1 & S2 MOGP VWC maps for the 2020 winter wheat site and 52 for 2021, we focused on the removed ranges to perform a posterior error metrics analysis. The global \(R^{2}\) values for the complete spatiotemporal regression were \(\overline{R^{2}}_{subset_{1}}\) = 0.72 and \(\overline{R^{2}}_{subset_{2}}\) = 0.67 and their related processing time: \(t_{subset_{1}}\) = 70 h and \(t_{subset_{2}}\) = 54 h approximately (processor: 11 th Gen Intel(R) Core(TM) i7-1195G7 @ 2.90 GHz 64 bits, RAM: 16 GB). The MOGP technique allowed the reconstruction of each channel in the S1 & S2 dataset independently. It means that, regardless of the acquisition dates of S1 and S2, the CONV model generated a VWC map that coincides with the S2 pass over the study area. Similarly, a reconstructed RVI map was also produced for each S1 acquisition date. The main advantage of MOGP is that there is no need for synchronicity between the acquisitions of S1 and S2. Although the MOGP regression models support the daily temporal resolution (the temporal resolution of each channel can be adjusted independently), we did not modify the S1 and S2 original observation dates. In summary, the temporal resolution of each reconstructed independent channel follows the same pattern as the acquisitions of S1 and S2 over the study area. Consequently, we obtained 44 S1 & S2 MOGP VWC maps for the 2020 wheat campaign and 52 for 2021. Figure 8 show the spatiotemporal reconstruction of VWC maps over the winter wheat paddocks of the BVCR 2020 crop campaign. The vegetation phenology (left) and the scatter plots (right) underpin the S1 & S2 MOGP VWC maps accurately for each assessment date. It is desirable to present the synergy results for the greenness stage of winter wheat at two different locations of the study site. Furthermore, the reconstructed VWC maps for the 2021 winter wheat site are shown in Figure 9. Seeking to prioritize the evaluation of the MOGP within the vegetative development stage of winter wheat plants, we calculated the average \(R^{2}\) values for the scatter plots presented in Figures 8 and 9, we obtained \(\overline{R^{2}}_{when4200}^{2}\) = 0.95 and \(\overline{R^{2}}_{when4201}^{2}\) = 0.96, which shows the strong correlation of S2 GP VWC and S1 & S2 MOGP VWC maps.