Data-Driven Soil Organic Carbon Sampling: Integrating Spectral Clustering with Conditioned Latin Hypercube Optimization

TL;DR

This paper introduces a hybrid spectral clustering and conditioned Latin hypercube sampling method to improve soil organic carbon sampling by ensuring diverse and representative environmental coverage, leading to better SOC prediction accuracy.

Contribution

The paper presents a novel hybrid approach combining spectral clustering with cLHS to enhance sampling representativeness in SOC monitoring, addressing limitations of existing methods.

Findings

Spectral-cLHS achieves more uniform covariate coverage than standard cLHS.

The method captures environmental heterogeneity more effectively.

Improved sampling design enhances SOC prediction accuracy.

Abstract

Soil organic carbon (SOC) monitoring often relies on selecting representative field sampling locations based on environmental covariates. We propose a novel hybrid methodology that integrates spectral clustering - an unsupervised machine learning technique with conditioned Latin hypercube sampling (cLHS) to enhance the representativeness of SOC sampling. In our approach, spectral clustering partitions the study area into $K$ homogeneous zones using multivariate covariate data, and cLHS is then applied within each zone to select sampling locations that collectively capture the full diversity of environmental conditions. This hybrid spectral-cLHS method ensures that even minor but important environmental clusters are sampled, addressing a key limitation of vanilla cLHS which can overlook such areas. We demonstrate on a real SOC mapping dataset that spectral-cLHS provides more uniform coverage of covariate feature space and spatial heterogeneity than standard cLHS. This improved sampling design has the potential to yield more accurate SOC predictions by providing better-balanced training data for machine learning models.