# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 6 Conclusions  
Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. In the study presented, we proposed a hybrid approach to estimate SOC remotely from Sentinel-2 satellite imagery. The approach relies on a novel two-stage pipeline, using a DNN (U-Net) trained to classify land cover to perform feature extraction and a different ML model to perform the SOC estimation. The proposed approach was evaluated on the largest dataset publicly available today, constructed of CHILSOC and LUCAS, and achieved results comparable to previous studies evaluated on several datasets that are orders of magnitude smaller and geographical regions, making it the first approach with the potential to scale to global SOC estimation and monitoring. \begin{table}
\begin{tabular}{l l l l} \hline
**Approach** & **Number of Samples** & **RMSE** & **R\({}^{2}\)** \\ \hline [PERSON] et al. [29] & 146 & 0.73 & 0.51 \\ [PERSON] et al. [30] & 705 & N/A & 0.47 \\ [PERSON] et al. [35] & 81 & 0.21 & 0.74 \\ [PERSON] et al. [40] & 733 & N/A & 0.26 \\ [PERSON] et al. [41] & 1879 & 0.75 & 0.65 \\ [PERSON] et al. [42] & 53–131 & 0.152 & 0.67 \\ Proposed on CHLSOC & 6900 & 2.33 & 0.7388 \\ Proposed on LUCAS 2015 & 11,061 & 2.5376 & 0.5233 \\ Proposed on LUCAS 2018 & 11,275 & 2.2506 & 0.6074 \\ Proposed overall & x & 2.5001 & 0.6384 \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison with published work. To demonstrate the usability of the proposed approach, we generated and shared a high-resolution map of SOC for the Italian region of Tuscany, in an effort to enable external validation of the methodology, as well as to stimulate the development of possible applications based on approaches such as the one presented in this paper. The work serves as a significant step toward implementing efficient, cost-effective, and remote SOC monitoring on a global scale. The demonstrated integration of satellite imagery and machine learning in SOC estimation opens a promising avenue for further research and practical applications, particularly in environmental science and land management. Conceptualization, M.P., S.I. and D.C.; Methodology, M.P., S.I. and D.C.; Software, M.P., S.I., D.W.R. and M.B. ; Validation, S.I., N.R., N.A. and D.C.; Formal analysis, S.I., N.R., N.A. and D.C.; Investigation, M.P. and S.I. ; Resources, M.P. and S.I. ; Data curation, M.P. and N.A. ; Writing--original draft, M.P. ; Writing--review & editing, S.I. and D.C.; Visualization, M.P. and N.A. ; Supervision, D.C.; Project administration, D.C.; Funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the article. Conflicts of Interest: Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Smart Cloud Farming. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. _Biogeosciences_**2013**, _10_, 1717-1736. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON] Global soil carbon: Understanding and managing the largest terrestrial carbon pool. _Carbon Manag._**2014**, \(5\), 81-91. [CrossRef]
* [PERSON] (2009) [PERSON] Harmonized soil profile data for applications at global and continental scales: Updates to the WISE database. _Soil Use Manag._**2009**, _25_, 124-127. [CrossRef]
* [PERSON] (1996) [PERSON] Total carbon and nitrogen in the soils of the world. _Eur. J. Soil Sci._**1996**, _47_, 151-163. [CrossRef]
* [PERSON] et al.