Summary
This research presents a hybrid framework integrating Earth Observation data processed through artificial intelligence with the RothC process-based model to estimate soil organic carbon stocks across Lithuania's entire agricultural area at high spatial resolution. The approach combines a custom cloud-based Soil Data Cube with climate projections to provide spatially explicit SOC change estimates under current management and future climate scenarios. The framework is positioned as a cost-effective, scalable tool for supporting EU agricultural policy and climate mitigation objectives.
UK applicability
The methodological approach (hybrid Earth Observation and RothC modelling) is directly transferable to UK croplands and could inform soil carbon monitoring under UK agricultural policy frameworks. However, soil type, climate, and management practices differ between Lithuania and the UK, necessitating local calibration and validation before policy application.
Key measures
Soil organic carbon stocks (tC/ha); annual SOC change rates (tC/ha/yr); spatial resolution maps of topsoil SOC; 20-year projections under three management/climate scenarios
Outcomes reported
The study mapped initial soil organic carbon stocks across Lithuanian croplands (ranging from 15 to over 80 tC/ha) and projected 20-year SOC changes under business-as-usual and climate change scenarios. Projections indicated average SOC loss of −0.14 tC/ha/yr under baseline conditions and accelerated depletion (−0.24 to −0.34 tC/ha/yr) under IPCC climate scenarios RCP 4.5 and 8.5.
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