Digital soil mapping in support of voluntary carbon market programs in agricultural land.

ABSTRACT: Voluntary carbon market (VCM) programs in agriculture depend on accurate measurements of soil organic carbon (SOC) that can be deployed at scale efficiently, but barriers are preventing widespread adoption. To overcome these challenges, we developed a digital soil mapping (DSM) framework driven by machine-learning and numerous spatial covariates, including long-term climate proxies, short-term climate and weather-related variables, topographic and edaphic measurements, and remote sensing time-series summaries. We show that the model can predict SOC content in the top 30 cm of soil using 5,230 measurements of SOC in agricultural land within 47 states in the contiguous United States (CONUS). Model predictions closely matched independent measured values. The intercept and slope of the cross-v

SUBMITTER: Kellner JR 

PROVIDER: S-EPMC12404560 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature