The objective of this study was to explore the feasibility and to investigate the mechanism for rapidly monitoring arsenic (As) contamination in agricultural soils with the reflectance spectra of rice plants. Several data pretreatment methods were applied to improve the prediction accuracy. The prediction of soil As contents was achieved by partial least-squares regression (PLSR) using laboratory and field spectra of rice plants, as well as linear regression employing normalized difference spectral index (NDSI) calculated from fild spectra. For laboratory spectra, the optimal PLSR model for predicting soil As contents was achieved using Savitzky-Golay smoothing (SG), first derivative and mean center (MC) (root-mean-square error of prediction (RMSEP) = 14.7 mg kg–1; r = 0.64; residual predictive deviation (RPD) = 1.31). For field spectra, the optimal PLSR model was also achieved using SG, first derivative and MC (RMSEP = 13.7 mg kg–1; r = 0.71; RPD = 1.43). In addition, the NDSI with 812 and 782 nm obtained a prediction accuracy with r = 0.68, RMSEP = 13.7 mg kg–1, and RPD = 1.36. These results indicated that it was feasible to monitor the As contamination in agricultural soils using the reflectance spectra of rice plants. The prediction mechanism might be the relationship between the As contents in soils and the chlorophyll-a/-b contents and cell structure in leaves or canopies of rice plants.