Heavy-metal pollution of crops seriously affects food safety and human health in China. The purpose of this study is to estimate the concentrations of heavy metals economically and accurately using spectroscopy. The spectral data of winter wheat canopy at 61 sampling points were collected in Longkou City, and the concentration of Pb, Zn, Cd, Cr, Ni, and Hg was measured. Eight estimation models were established based on the back-propagation neural network (BPNN), the partial least squares regression, and the stepwise multiple linear regression methods combined with reflectance spectra, first-order derivative of spectral reflectance (FDR), second-order derivative of spectral reflectance, and spectral parameters (SPs). Spatial interpolation was used to map the heavy-metal concentration. The results showed that the best model for Pb, Zn, and Cd was the BPNN model with SPs, for Cr, Ni, and Hg was the BPNN model with FDR. The concentration of Hg was lower in the northwest, Pb and Zn were higher in the middle and south, Cd was lower in the middle and west, and Cr and Ni were higher in the southeast. This study will provide effective technical support for the economical and accurate monitoring of heavy metals.