Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network

TL;DR

This study demonstrates that Graph Neural Networks, especially PESAGE and PETransformer, effectively estimate soil organic carbon from climate-related features, offering a promising approach for improved land management and climate modeling.

Contribution

It introduces the application of GNNs with positional encoders for SOC estimation, comparing four models and identifying the most effective architectures.

Findings

PESAGE and PETransformer outperform other GNN models in SOC estimation.

GNN architectures effectively capture complex soil-climate relationships.

Framework established for future advanced GNN applications in SOC prediction.

Abstract

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.