Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

International Journal of Applied Earth Observation and Geoinformation

Volumn 52, Issue , 2016, Pages 95-103

  Shi, Tiezhu ^a   Wang, Junjie ^a   Chen, Yiyun ^b   Wu, Guofeng ^a  

^a SHENZHEN UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Genetic algorithm; Leaf chlorophyll; Paddy soil; Partial least squares regression; Soil organic matter; Visible and near infrared reflectance spectroscopy

Indexed keywords

AGRICULTURAL SOIL; ARSENIC; CHLOROPHYLL; GENETIC ALGORITHM; LEAF; LEAST SQUARES METHOD; SOIL ORGANIC MATTER; SPECTRAL REFLECTANCE;

EID: 84997784995     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2016.06.002     Document Type: Article

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