To create these maps, the researchers applied advanced machine learning technique that modeled the spatial and temporal changes of SOC based on topographical, climatic, and land use factors. The results not only show the distribution of SOC but also quantify the associated uncertainty, thus supporting scientifically sound decision-making.
The resulting maps are freely available and can serve as valuable tools for sustainable land use, rural development, ecosystem services assessment, etc. These findings may contribute to achieving the goals of the Soil Monitoring Law, the European Green Deal, and other international sustainability initiatives, promoting more effective soil protection and carbon sequestration.
