Soil carbon sequestration is an important component of global carbon balance in the context of ameliorating the effects of carbon dioxide emissions. Remote sensing methods based upon hyperspectral quantification of soil reflectance can provide rapid and cost-effective assessment of soil properties including soil carbon content. This project evaluated five such methods. Various remote sensing technologies were used to collect reflectance data from five tilled agricultural fields on Maryland's Eastern Shore. They included: 1) airborne Near Infra-Red (NIR) hyperspectral reflectance imagery (2.5-m pixel resolution); 2) in-field tractor-based measurement of surface soil properties (0-20-cm) using NIR reflectance and electrical conductivity; 3) Mid Infra-Red (MIR) hyperspectral reflectance measurements of field-moist soil samples; and 4,5) MIR and NIR hyperspectral reflectance measurements of dried, ground soil samples. Soil carbon content (dry combustion) was measured for 420 soil samples (0-20 cm) collected along the in-field transects that were sampled by the tractor-based NIR equipment. Partial Least Squares (PLS) regression methods were used to correlate observed soil carbon content values with predictions derived from the various reflectance datasets, and comparisons among the remote sensing technologies were calculated based upon their success in predicting carbon contents for a subset of soil samples reserved for validation purposes.