Abstract: In order to effectively control the saline soil, taking Songliao Basin for example, the Environmental Mitigation Satellite (HJ-1A) hyperspectral data was used in this study. The most suitable quantitatively retrieve model of saline soil was selected by comparing the forecast results of the salt-bearing rate content retrieved by curvilinear regression and least squares support vector machine (LS-SVM) regression. Ultimately, LS-SVM regression was chosen to retrieve various saline soil indexes in Daqing where the soil was salinized seriously. The retrieve results were classified into several grades by binary decision tree. The results showed that, it was convenient and effective to acquire the saline soil information by using HJ-1A. The accuracy of retrieve model based on LS-SVM was high. The saline soil grade classification, which was calculated by binary decision tree using the RS technology, was accurate and reliable. Soil salinization of Daqing was serious, most of that was alkali soil. The area of light, medium and server alkali soil was separately 345.03, 1?389.03, 869.94?km2 , respectively. The research has great significance for saline soil rapid extracting and prevention in Songliao Basin.