Modeling and Verification of Soil Salt Content Based on Hyperspectral Characteristic Parameter Optimization

【Objective】 To explore the feasibility of soil salinity inversion model under different dimensional spectral transformations. 【Methods】 The lakeside oasis on the west of Bosten Lake in Xinjiang was taken as the research area, the correlation analysis between 17 one-dimensional mathematical transformation spectra and 3 two-dimensional transformation spectral indexes of ASD hyperspectral data and the measured soil salinity were conducted to obtain the preliminarily optimized spectral characteristic parameters at the significance test level of 0.01. Then, the PLSR model was constructed based on the VIP criteria and selected into the optimal independent variable and the accuracy was verified. 【Results】 The average reflectance of dry soil was higher than that of wet season with the increase of salt content, especially at 590, 800, 1,810 nm and 2,150 nm. Among the 17 one-dimensional single-band spectral transformations, the first derivative of logarithmic reciprocal (1/lgR) had the best correlation with soil salinity, the peak sensitive band was 1083nm, and the absolute value of correlation coefficient was up to 0.63. Among the three two-dimensional two-band spectral transforms, the normalized spectral index NDSI(R_{1 780}, R_{1 742}) had the best correlation with soil salt content, and the maximum value of correlation analysis determination coefficient R ² was 0.57. The PLSR estimation model based on characteristic normalized spectral index and VIP criterion for independent variable screening had the best effect. The determination coefficient ${R_{\mathrm{V}}}^2$ of soil salt modeling set and verification set was 0.77, the root mean square error was 0.64 g/kg, and the relative analysis error was 2.11. 【Conclusion】 Using the normalized spectral index (NDSI) to establish PLSR hyperspectral model could effectively estimate the soil salinity in the study area.