## 5 Conclusions  
This study assessed a new approach for using a time series of Sentinel-2 data combined with Sentinel-1-derived data and Vis-NIR laboratory spectra to predict the SOC content of agricultural soils and tested predictive models of three different algorithms. To retrieve the relevant information contained in the images, all Sentinel-2 spectra that corresponded to bare soil for each soil sample were grouped into a table, which can provide an alternative to mosaicking approaches. Our results showed the following:  
The DNN models outperformed the PLS and RF models and the inclusion of additional information (i.e., S2-indices, S1-indices, and S1-soil moisture) influenced the prediction performance of the DNN models more than that of the RF models. Although the inclusion of additional information improved prediction performance during model calibration, it did not influence model validation. Furthermore, S1-soil moisture gave the most stable improvement in calibration and the closest validation performance to that of models calibrated using only S2 bands, which had the lowest RMSEP. As the Lab-indices showed strong and significant correlations with measured SOC content, their incremental addition to the models improved the prediction of SOC content, and the addition of only two indices yielded good performances. Future research could evaluate the approach developed for study areas that have more available Sentinel-2 data and different soil and climate conditions to assess its potential to be generic. Accordingly, the principal objective of this study was to improve the accuracy of SOC content prediction using Sentinel-2 data, with the ultimate goal of producing highly detailed SOC maps. The availability of these maps will be of great help in the crucial issue of monitoring agricultural soils in relation to agricultural practices. Conceptualization, H.Z., Y.F., D.M. and C.W. ; methodology, H.Z., Y.F., D.M. and C.W. ; software, H.Z. ; formal analysis, H.Z., Y.F., D.M. and C.W. ; writing--original draft preparation, H.Z. ; writing--review and editing, Y.F., D.M., C.W., N.B. and E.V. ; visualization, H.Z., Z.K. and Z.L.-C.; supervision, Z.K., Z.L.-C., Y.F., D.M. and C.W. ; funding acquisition, E.V., Y.F., Z.K. and Z.L.-C. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was supported by the European Union's Horizon H2020 research and innovation European Joint Programme Cofund on Agricultural Soil Management (EJP-SOIL grant no. 862695) and was carried out in the framework of the STEROPES project of EJP-SOIL. It was also supported by the MELCERTES project (ANR-22-PEAE-0010) of the French National Research Agency, under the France2030 program and the national PEFR \"agroecologie et numerique\" program, and the French-Tunisian project PHC-Utique IPASS (44324 WB/20G093). The lead author was awarded a Ph.D. grant by the Tunisian Republic. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** The authors gratefully acknowledge financial support from Rennes Metropole. We also thank all the technical teams for their strong collaboration and support in performing the ground-truth measurements. **Conflicts of Interest:** The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**RF**} & \multicolumn{4}{c}{**DNN**} \\
**Input** & **It.