Hyperspectral predicting model of soil salinity in Tianjin costal area using partial least square regression

Soil salinization is one of the most devastating land degradation process causing agricultural yields reduction. This paper presents a hyperspectral prediction model of soil salinity using partial least squares regression (PLSR) in Tianjin costal area. Soil spectral reflectance of soil samples varying in salinity was measured using an ASD Field Spec spectrometer. The treated continuum-removed (CR) reflectance and first-order derivative reflectance (FDR) were used and compared to explore the more preferable predicting model of soil salinity, which could detect subtle differences in spectral absorption features compared with original reflectance. The results showed that the soil spectra reflectance got distinct absorption feature with peaks centred at 411 nm, 475 nm, 663 nm, 868 nm, 1100 nm ~ 1250 nm, 1400 nm, 690 nm, 1911 nm, 2206 nm and 2338 nm, representing key bands for soil salt content estimation. Through established Partial Least-Square Regression model based on treated soil spectra, the first derived-continuum-removed reflectance was the optimal spectra indexes, prediction accuracy of the optimal PLSR model was 94.4%.