Soil information is needed for environmental monitoring to address current concerns over food, water and energy securities, land degradation, and climate change. We developed the Soil Condition ANalysis System (SCANS) to help address these needs. It integrates an automated soil core sensing system (CSS) with statistical analytics and modeling to characterize soil at fine depth resolutions and across landscapes. The CSS's sensors include a γ-ray attenuation densitometer to measure bulk density, digital cameras to image the measured soil, and a visible-near-infrared (vis-NIR) spectrometer to measure iron oxides and clay mineralogy. The spectra are also modeled to estimate total soil organic carbon (C), particulate, humus, and resistant organic C (POC, HOC, and ROC, respectively), clay content, cation exchange capacity (CEC), pH, volumetric water content, available water capacity (AWC), and their uncertainties. Measurements of bulk density and organic C are combined to estimate C stocks. Kalman smoothing is used to derive complete soil property profiles with propagated uncertainties. The SCANS provides rapid, precise, quantitative, and spatially explicit information about the properties of soil profiles with a level of detail that is difficult to obtain with other approaches. The information gained effectively deepens our understanding of soil and calls attention to the central role soil plays in our environment.