Topsoil organic carbon estimations in greece via deep learning and open earth observation data

This paper presents a novel methodology for estimating crop-land topsoil organic carbon (SOC) content using open-access Earth Observation (EO) data and deep learning techniques in Greece. We address the scarcity of ground-truth reference data using a data augmentation technique utilizing spatial neighbors. By incorporating neighboring pixel information in a 3x3 grid (corresponding to 30 x 30 m meters in Sentinel-2), we achieve robust predictions and provide uncertainty estimations for SOC distribution in Greek croplands. Experimental results demonstrate the superiority of deep learning techniques over conventional methods in terms of estimation accuracy (RMSE=3.51, R-2=0.59, RPIQ=2.21). This methodology offers a cost-effective and efficient solution for directing land management practices to enhance soil health and mitigate carbon emissions.