研究盐碱化土壤的光谱特性是利用遥感技术实现在区域尺度上进行土壤盐碱化监测和评价的工作基础。 为了实现对内蒙古河套灌区土壤盐分的定量反演, 于2007年7～8月间采集了河套灌区土壤样品, 进行土壤化学成分及其光谱反射特性的测量。 基于统计分析的方法, 建立了土壤盐分与高光谱数据的偏最小二乘回归（PLSR）模型, 对土壤中主要盐分参数进行了反演实验。 独立验证结果表明, 对全盐(S%)、 硫酸根离子（SO2-4）、 pH值以及钾、 钠总含量（Ka++Na+）有较好的反演精度, 验证数据的决定系数R2分别是0.728, 0.801, 0.715和0.734, 预测方差比（RDP）分别是1.79, 1.87, 1.64, 1.63。 将以上参数的PLSR模型回归系数聚合在TM的可见光（蓝色、 绿色、 红色）和近红外波段时, 回归系数在数值上均有显著反应。 研究结果为在航空航天遥感尺度上实现土壤盐分的定量反演提供了理论基础和实验依据。

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%), SO2+4, 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.