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

Description

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 mon itoring using open access Copernicus data. In particular, we employ data from the LUCAS topsoil database, ERA5 cli mate data from the Copernicus Climate Data Store, and the EU-DEMfromtheCopernicusLandMonitoring Service. Us ing Recursive Feature Elimination and the Random Forest al gorithm, 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 of0.66for topsoil Clay content. Our method enhances spatio-temporal representativeness and reli ability, aligning with the European Union's policies like the Common Agricultural Policy, the new green deal, and eco schemes. 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.

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