University of Illinois researchers showed in a published study that new machine-learning methods based on laboratory soil hyperspectral data could provide accurate estimates of soil organic carbon. A press release explains that the study "provides a foundation to use airborne and satellite hyperspectral sensing to monitor surface soil organic carbon across large areas." This would be faster and easier than performing chemical analyses to measure soil organic carbon content, and it works at a large scale.
