title: Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice.
abstract: 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 three-band 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.
authors: Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-information & Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services & College of Life and Marine Sciences, Shenzhen University, 517920 Shenzhen, China.
authors: Tiezhu
authors: T
authors: Shi
authors: Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-information & Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services & College of Life and Marine Sciences, Shenzhen University, 517920 Shenzhen, China.
authors: Huizeng
authors: H
authors: Liu
authors: School of Resource and Environmental Sciences, Wuhan University, 430079 Wuhan, China.
authors: Yiyun
authors: Y
authors: Chen
authors: Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-information & Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services & College of Life and Marine Sciences, Shenzhen University, 517920 Shenzhen, China.
authors: Junjie
authors: J
authors: Wang
authors: Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-information & Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services & College of Life and Marine Sciences, Shenzhen University, 517920 Shenzhen, China; School of Resource and Environmental Sciences, Wuhan University, 430079 Wuhan, China. Electronic address: guofeng.wu@szu.edu.cn.
authors: Guofeng
authors: G
authors: Wu
mesh_terms: D000465:Algorithms; D001151:Arsenic / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D016014:Linear Models; D019216:Metals, Heavy / Q000032:analysis; D012275:Oryza / Q000254:growth & development*; D012989:Soil Pollutants / Q000032:analysis*; D013057:Spectrum Analysis
chemical_list: D019216:Metals, Heavy; D012989:Soil Pollutants; D001151:Arsenic
keywords: Arsenic; Photochemical reflectance index; Red edge position; Three-band vegetation index; Two-band vegetation index