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Classically, scientists gather soil samples in the field and transport them back to the lab, where they examine the material to establish its constituents. But that is time-intensive, laborious, expensive and only offers insights on particular locations.
In a new study, University of Illinois scientists demonstrate new machine-learning techniques based on laboratory soil hyperspectral data that could deliver equally accurate approximations of soil organic carbon. Their study offers the groundwork to use airborne and satellite hyperspectral sensing to track surface soil organic carbon spanning large areas.