A Hybrid Sampling-Modelling Approach using Direct Measurement and Remote Sensing to Optimise the Cost-Uncertainty Balance in Large Scale Carbon Monitoring and Carbon Farming Projects.

Effective, reliable, and cost-efficient soil carbon monitoring remains a critical bottleneck for the credibility of carbon farming projects. Large-scale projects are particularly problematic since soil sampling campaigns that enable monitoring are often logistically and financially challenging.  

Current carbon reporting protocols rely predominantly on monitoring supported by direct measurement of soil carbon stocks, often requiring stratified random sampling (SRS) across the project area. Although unbiased, SRS scales poorly, both logistically and financially, and quickly becomes unfeasible for large projects. Alternatives, often using Digital Soil Mapping (DSM) and remote sensing, are being used increasingly. While appearing to be more cost-effective since they generally entail collecting fewer soil samples, these alternatives increase uncertainty in reporting soil carbon, jeopardising the ability to reliably detect real change and risking trust in carbon farming projects.  

We propose a hybrid sampling-modelling alternative that integrates a cost-effective stage-sampling approach with a Bayesian areal spatial model that uses remote-sensing data to jointly optimise soil sampling costs and predictive uncertainty.  The areal spatial model is a latent Gaussian model fitted using integrated nested Laplace approximations (INLA) in a hierarchical Bayesian framework. The model uses remote-sensing covariates and in situ measurements to predict soil carbon stocks in regions not sampled during the sampling process. The result is a hybrid dataset that combines direct-measurement and model predictions with quantified uncertainty that can be used for accurate and reliable carbon monitoring or as input for other models.  

We present the results of a simulation study that quantifies the trade-offs between cost, number of samples and total uncertainty from the sampling design and the areal spatial model. We also present a case study of a 170-farm project in the United Kingdom, where we demonstrate the feasibility, cost-savings, and uncertainties of the approach. The results are compared to direct measurement, remote sensing data and DSM estimates to show that this framework offers a practical and cost-effective alternative that results in optimal uncertainties for carbon reporting.