In a comprehensive review of regional case studies, researchers found that increasing surface soil carbon stocks by just 0.4% globally could offset 20-35% of global greenhouse gas emissions. In addition, increased levels of soil carbon are well correlated to higher net primary productivity, greater stability of agricultural yields, and improved soil water retention. Non-profits and government agencies have begun to look for ways to incentivize managers to transition to land management practices that might increase soil carbon, but monitoring progress of such initiatives is difficult given the extremely high cost of traditional laboratory based methods for measuring soil carbon. We have recently developed a measurement protocol that makes use of low-cost field spectrometers and freely available spatial datasets to estimate soil carbon content at individual sample points. First, we sample soil throughout a given site to capture a range of soil carbon contents using traditional, highly accurate lab-based techniques. Those data are then used to build statistical models relating lab-measured soil carbon levels to the spectral data collected with the field spectrometer. These models are further improved by integrating different data types that may be predictive of soil carbon levels from spatially explicit datasets. After the initial site-specific calibration, carbon content can be determined in the field using only the pocket spectrometer and the previously developed model, dramatically reducing sample collection time and cost and allowing managers to sample more frequently in the future and over broader areas. With these reductions in cost, managers can quantify soil carbon at landscape scales and at more frequent intervals, allowing them to inexpensively monitor soil carbon responses as new management practices are adopted.