Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China

Hongnan Jiang; Hong Shu; Lei Lei; Jianhui Xu

doi:10.1117/1.JRS.11.016043

31 March 2017 Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China

Hongnan Jiang, Hong Shu, Lei Lei, Jianhui Xu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 016043 (March 2017). https://doi.org/10.1117/1.JRS.11.016043

Abstract

HJ-1A hyperspectral data were used to distinguish topsoil salt components and estimate soil salinity, and the relationship between soil salt chemical components and sensitive bands of soil reflectance spectra was analyzed. The correlation between the soil salt content and the soil spectra obtained from the hyperspectral data was analyzed, proving that topsoil salinity has a very significant correlation with soil reflectance spectra. The relationship between soil reflectance spectra and salt chemical ions was investigated. The soil spectral reflectance at wavelength 510.975 nm and a difference vegetation index were selected to estimate soil salinity and the dominant salt chemical ion concentrations at a depth of 0 to 10 cm using a partial least squares regression model. It was found that the bands sensitive to various levels of chemical components of soil salt were shown to differ, controlled by the dominant component of the soil salt. The sensitive bands in the soil salinity estimation will change with differences in salt components. Estimating the dominant salt in the soil using soil reflectance spectra will lead to greater prediction accuracy. This study provided a possible method for the estimation of salinity and chemical component levels in topsoil, using the hyperspectral data to estimate topsoil salt components.

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Hongnan Jiang, Hong Shu, Lei Lei, and Jianhui Xu "Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China," Journal of Applied Remote Sensing 11(1), 016043 (31 March 2017). https://doi.org/10.1117/1.JRS.11.016043

Received: 7 October 2016; Accepted: 12 January 2017; Published: 31 March 2017

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