Abstract: The quantitative inversion of soil organic carbon (C_org) in the study area is conducted by using multiple stepwise regression analysis method in combination with Landsat8 OLI remote sensing data. For the test, 164 soil samples are collected. Singular points are removed and data sets are divided by tripled standard deviation. Among the total, 120 samples are chosen as the training set and the other 44 as the validation set to establish the multiple stepwise regression prediction model for C_org. The results show that the organic carbon is significantly correlated with the reflectivity of Landsat8 bands. The optimal model for the prediction of black soil organic carbon spectrum is the one that takes the reciprocal as the independent variable, with the determination coefficient R²=0.180, and root-mean-square error(RMSE)=0.558. Hailun area is suitable for remote sensing inversion of C_org content, with a stable prediction model, which can be used to reveal the spatial distribution of C_org content in typical black soil areas. Meanwhile, it is believed that without ground spectral test for the soil, the fitting degree of prediction model by simply using the method of associating chemical analysis data with remote sensing satellite is limited and the interpretation of C_org by spectrum is poor.