[Quantitative retrieval of soil salinity using hyperspectral data in the region of inner Mongolia hetao irrigation district].

In the present paper, to investigate the spectral property of salinized soil and the relationship between the soil salinity and the hyperspectral data, the field soil samples were collected in the region of Hetao irrigation, Neimeng in the northwest China from the end of July to the beginning of August. The partial least squares regression (PLSR) model was established based on the statistical analysis of the soil ions and the reflectance of hyperspectra. The independent validation using data which are not included in the calibration model reveals that the proposed model can predicate the main soil components such as the content of total ions (S%), SO4(2+), PH and K+ + Na+ with higher determination coefficients (R2) Of 0.728, 0.801, 0.715 and 0.734 respectively. And the ratio of prediction to deviation (RPD) of the above predicted value is larger than 1.6, which indicates that the calibrated PLSR model can be used as a tool to retrieve soil salinity with accurate results. When the PLSR model's regression coefficients were aggregated according to the wavelength of visual (blue, green and red) and near infrared bands of LandSat Thematic Mapper(TM) sensor, some significant response values were observed, which indicates that the proposed method in this paper can be used to analyse the remotely sensed data from the space-boarded platform.