Abstract

Accurate quantification of soil organic carbon (SOC) is critical for mitigation policy design, carbon credit generation, and the transition to regenerative agricultural practices. This systematic review synthesizes emerging technologies, artificial intelligence (AI) models, and methodological approaches for SOC estimation. We highlight advances in accuracy and scalability achieved through the integration of remote sensing, in situ sensing, geographic information systems (GIS), and data derived from omics analyses. The findings underscore the superior performance of hybrid approaches that fuse multiple data sources with explainable AI (XAI) models. Collectively, these innovations enable reliable, scalable measurement, reporting, and verification (MRV) systems that are essential for agricultural sustainability and climate-change mitigation.