Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice.

Abstract This study systematically analyzed the performance of multivariate hyperspectral vegetationindices of rice ( Oryza sativa L. ) in estimating the arseniccontent in agricultural soils. Field canopy reflectance spectra was obtained in the jointing-booting growth stage of rice. Newly developed and published multivariate vegetationindices were initially calculated to estimate soil arseniccontent. The well-performing vegetationindices were then selected using successive projections algorithm (SPA), and the SPA selected vegetationindices were adopted to calibrate a multiple linear regression model for estimating soil arseniccontent. Results showed that a three-band vegetation index( R 716 − R 568 )/( R 552 − R 568 ) performed best in the newly developed vegetationindices in estimating soil arseniccontent. The photochemical reflectance index(PRI) and red edgeposition (REP) performed well in the published vegetationindices. Moreover, the linear combination of two vegetationindices (( R 716 − R 568 )/( R 552 − R 568 ) and REP) selected using SPA improved the estimation of soil arseniccontent. These results indicated that the newly developed three-band vegetation index( R 716 − R 568 )/( R 552 − R 568 ) might be recommended as an indicator for estimating soil arseniccontent in the study area. PRI and REP could be used as universal vegetationindices for monitoring soil arseniccontamination.