This study systematically analyzed the performance of multivariate hyperspectral vegetation indices of rice (Oryza sativa L.) in estimating the arsenic content in agricultural soils. Field canopy reflectance spectra was obtained in the jointing-booting growth stage of rice. Newly developed and published multivariate vegetation indices were initially calculated to estimate soil arsenic content. The well-performing vegetation indices were then selected using successive projections algorithm (SPA), and the SPA selected vegetation indices were adopted to calibrate a multiple linear regression model for estimating soil arsenic content. Results showed that a three-band vegetation index (R716 − R568)/(R552 − R568) performed best in the newly developed vegetation indices in estimating soil arsenic content. The photochemical reflectance index (PRI) and red edge position (REP) performed well in the published vegetation indices. Moreover, the linear combination of two vegetation indices ((R716 − R568)/(R552 − R568) and REP) selected using SPA improved the estimation of soil arsenic content. These results indicated that the newly developed threeband vegetation index (R716 − R568)/(R552 − R568) might be recommended as an indicator for estimating soil arsenic content in the study area. PRI and REP could be used as universal vegetation indices for monitoring soil arsenic contamination.