Soil-based carbon sequestration has attracted substantial attention due to its massive potential capacity and relative ease of implementation. However, scalable and accurate soil measurement methods have been elusive, making verification of sequestration a notable barrier. We present an ML-based approach relying on multispectral satellite imagery as a scalable, robust measurement methodology for soil carbon on farmland. The solution performs above existing standards under testing in the USA and Australia, and at a much lower cost. The audience will learn the background on carbon credit markets and soil-based sequestration, as well as select model implementation details and real-world test results.