An Agricultural Hybrid Carbon Model for National-Scale SOC Stock Spatial Estimation

Abstract

Soil Organic Carbon (SOC) stocks in croplands play a key role for climate change mitigation and soil sustainability, with proper management techniques enhancing carbon storage to support these goals. This study focuses on the development of a hybrid carbon modeling approach for the simulation of topsoil SOC stocks across the entire agricultural area of Lithuania. In essence, the proposed hybrid approach combines a custom cloud-based Soil Data Cube (SDC) and the RothC process-based model. High-resolution annual soil layers produced via the SDC (developed using Earth Observation and Copernicus datasets processed through AI-based methodologies) were incorporated into the RothC model to achieve reliable and detailed spatial estimations of SOC stocks. Moreover, 20-year projections into the future were conducted for (i) the business as usual scenario, and (ii) two different IPCC climate change scenarios (RCP 4.5 and 8.5) for the estimation of the SOC stock changes. The initial SOC stock varies from 15 to over 80 (Formula presented.) while the projections present an average SOC loss of (Formula presented.) (Formula presented.) or the business-as-usual scenario and an average SOC sequestration of (Formula presented.) and (Formula presented.) (Formula presented.) under RCP 4.5 and RCP 8.5, respectively. The framework aims to provide a robust and cost-effective solution for estimating SOC stocks under climate pressures, supporting EU policies such as the Common Agricultural Policy.