Because of frequent mining, heavy metals are brought into environment like soils, water and atmosphere, resulting heavy metal contamination in the agricultural region beside mines. Heavy metals contamination causes vegetation stress like destruction of chloroplast structure, chlorophyll content decrease, blunt photosynthesis, etc. Spectral responses to changes in chlorophyll content and photosynthesis make it possible that remote sensing is applied in monitoring heavy metals stress on paddy plants. Field spectroradiometer was used to acquire canopy reflectance spectra of paddy plants contaminated by heavy metals released from local mining. The present study was conducted to (1) investigate discrimination of canopy reflectance spectra of heavy metal polluted and normal paddy plants; (2) extract spectral characteristics of contaminated paddy plants and compare them. By means of correlation analysis, sensitive bands (SB) were firstly picked out from canopy spectra. Secondly, on the basis of these sensitive bands, normalized difference vegetation indices (NDVI) were established, and then red edge position (REP) was extracted from canopy spectra via curve fitting of inverted Gaussian model. As a result of correlation analysis, 460, 560, 660 and 1 100 nm were considered respectively as sensitive band for Pb, Zn, Cu and As concentration in paddy leaves. Furthermore, heavy metal concentrations (Pb, Zn, Cu and As) were significantly correlated with NDVIs (Pb, NDVI(510, 810)； Zn, NDVI(510, 870)； Cu, NDVI(660, 870); As, NDVI(510, 810)). Heavy metals were also significantly correlated with REP, however, the inflexion termed as spectral critical value (SCV) between low and high heavy metals concentrations should be considered during applying REP in remote sensing monitoring. Moreover, NDVI and REP are much better than SB in terms of capability of expressing spectral information. Therefore, heavy metals contamination in paddy plants can be remotely monitored via ground spectroradiometer when NDVI and REP are selected as spectral characteristics.