Shi Tiezhu, Liu Huizeng, Chen Yiyun, Wang Junjie, Wu Guofeng
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.
J Hazard Mater. 2016 May 5;308:243-52. doi: 10.1016/j.jhazmat.2016.01.022. Epub 2016 Jan 21.
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.
本研究系统分析了水稻（Oryza sativa L.）多元高光谱植被指数在估算农业土壤砷含量方面的性能。在水稻拔节孕穗期获取田间冠层反射光谱。最初计算新开发和已发表的多元植被指数以估算土壤砷含量。然后使用连续投影算法（SPA）选择性能良好的植被指数，并采用经SPA选择的植被指数来校准用于估算土壤砷含量的多元线性回归模型。结果表明，在新开发的植被指数中，三波段植被指数（R716 - R568）/（R552 - R568）在估算土壤砷含量方面表现最佳。在已发表的植被指数中，光化学反射指数（PRI）和红边位置（REP）表现良好。此外，使用SPA选择的两个植被指数（（R716 - R568）/（R552 - R568）和REP）的线性组合提高了土壤砷含量的估算效果。这些结果表明，新开发的三波段植被指数（R716 - R568）/（R552 - R568）可能被推荐作为研究区域土壤砷含量估算的指标。PRI和REP可作为监测土壤砷污染的通用植被指数。
