Employing the Soil Data Cube and Digital Soil Mapping Techniques for National Topsoil Predictions of Soil Organic Carbon and Clay Content over the Lithuanian Grasslands

Grasslands store a large fraction of terrestrial carbon, but are susceptible to degradation from anthropogenic disturbances and climatic changes. Soil monitoring can aid in conserving their ecosystem services. To overcome limitations posed by existing soil maps (e.g., low spatial resolution), we leverage the Soil Data Cube and Digital Soil Mapping techniques, to develop a cloud-optimized pipeline for large-scale soil monitoring using open access Copernicus data. In particular, we employ data from the LUCAS topsoil database, ERA5 climate data from the Copernicus Climate Data Store, and the EU-DEM from the Copernicus Land Monitoring Service. Using Recursive Feature Elimination and the Random Forest algorithm, the methodology achieves an RMSE of 49.1 g C / kg and an R² of 0.66 for topsoil Organic Carbon, and an RMSE of 52.1 g / kg with an R² of 0.66 for topsoil Clay content. Our method enhances spatio-temporal representativeness and reliability, aligning with the European Union’s policies like the Common Agricultural Policy, the new green deal, and ecoschemes. The outcomes of this study are the production of high-resolution soil maps tailored to Lithuanian grasslands. These advancements in soil health monitoring empower more effective and sustainable soil management practices.