Soil organic carbon (SOC) plays a pivotal role in the global carbon cycle, impacting climate dynamics and necessitating accurate estimation for sustainable land and agricultural management. While traditional methods of SOC estimation face resolution and accuracy challenges, recent technological solutions harness remote sensing, machine learning, and high-resolution satellite mapping. Graph Neural Networks (GNNs), especially when integrated with positional encoders, can capture complex relationships between soil and climate. Using the LUCAS database, this study compared four GNN operators in the positional encoder framework. Results revealed that the PESAGE and PETransformer models outperformed others in SOC estimation, indicating their potential in capturing the complex relationship between SOC and climate features. Our findings confirm the feasibility of applications of GNN architectures in SOC prediction, establishing a framework for future explorations of this topic with more advanced GNN models.

翻译：土壤有机碳（SOC）在全球碳循环中发挥着关键作用，影响气候动态，因此需要精准估算以实现可持续的土地与农业管理。传统SOC估算方法面临分辨率和精度挑战，而近年来的技术解决方案利用了遥感、机器学习和高分辨率卫星绘图。图神经网络（GNN）尤其在集成位置编码器时，能够捕捉土壤与气候之间的复杂关系。本研究利用LUCAS数据库，在位置编码器框架下比较了四种GNN算子。结果表明，PESAGE和PETransformer模型在SOC估算中表现优于其他模型，凸显了它们捕捉SOC与气候特征复杂关系的潜力。我们的发现证实了GNN架构在SOC预测中应用的可行性，为未来使用更先进GNN模型探索该课题奠定了基础。