Soil Organic Carbon Estimation from Climate-related Features with Graph
  Neural Network
Weiying Zhao Natalia Efremova
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.