# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 4 Experiments and Results
### Constructing the SOC Map: Application of the Model and Mosaicking Approach  
In the final stage of our pipeline, we used our mosaicking approach to produce a continuous, high-resolution SOC map. Each input image fed to the first stage of our pipeline was 64 \(\times\) 64 px, with each pixel representing a 10 \(\times\) 10 m area on the ground. All 13 spectral bands of Sentinel-2 were used. The model processes each input image and produces an output of the same 64 \(\times\) 64 px size. However, each output image is padded by 8 px on all sides, resulting in an valid output image size of 48 \(\times\) 48 px. This padding strategy was employed to account for the model's decreasing capacity to maintain context as it approaches the borders of the input image. To form the final continuous SOC map, we employed a sliding window technique. This technique ensures that the 48 \(\times\) 48 px output images (tiles) are perfectly adjacent to each other, resulting in a seamless, continuous map, shown in Figure 7. The application of this technique requires careful consideration of the input image dimensions, output image dimensions, and padding to ensure accurate alignment and continuity of the final SOC map. By employing this methodology, we were able to leverage the predictive power of our model across the landscape, generating an accurate, high-resolution, and continuous representation of SOC content across Tuscany. Figure 7: Sample input patches taken for the region of Tuscany. Once the map was generated, we used the CORINE dataset to create a validity mask for our SOC map. In particular, CLC2018 was used, the publicly available and up-to-date version of CORINE. The CLC2018 data were used in their native raster format, to eliminate SOC estimates for land cover classes that our model was not trained for. We restricted our estimates to categories 2 and 3, as shown in Table 7 (Agricultural areas and Forest and semi-natural areas), as regions of interest within the study area, while everything else was considered as an area with invalid data. The result is a raster layer of data that clearly separates cultivated areas and areas under vegetation from built-up areas and areas under water. Product Generation and Availability  
In the execution of our methodology, we successfully generated a high-resolution, continuous soil organic carbon (SOC) map for the region of Tuscany. The SOC map, alongside the CORINE-based validity mask, provides a valuable resource for understanding SOC distribution relative to different land use and land cover classes. These products, formatted as GeoTIFF files, can be easily integrated into further spatial analyses. To facilitate the use of these data products, we provide Python examples demonstrating how to interact with these files in our GitHub repositories. These repositories contain not only the data, but also detailed guidance for engaging with the data using popular geospatial Python libraries. To visually grasp the SOC distribution across Tuscany, please refer to Figure 8. This figure, entitled \"Grayscale representation of SOC values with validity overlay\", illustrates SOC content across the region with an overlay of the validity mask. The tools and data we generated promise to support future research endeavors and inform sustainable management practices in Tuscany's rich agricultural landscapes. For this purpose, we share our code at [[https://github.com/iai-rs/soc](https://github.com/iai-rs/soc)]([https://github.com/iai-rs/soc](https://github.com/iai-rs/soc)) (accessed on 4 January 2024).