Soil moisture has a significant impact on soil spectral reflectance, while it was rarely involved in modeling for remote-sensing-inversion-based mapping of soil organic matter in the past. In order to improve the accuracy of spatial prediction of soil organic matter, by taking into full account the characteristics of soil sampling sites, such as spatial autocorrelation, independence and complex field environment, the paper gathered via geostatistis soil moisture spatial distribution data in the study area, based on which in combination of remote sensing reflectance a multivariable prediction model was built up and a soil organic matter spatial distribution map of the black soil region in Jilin Province was plotted. Results show that in remote-sensing mapping of soil organic matter, the involvement of soil moisture as a variable, made the model more consistent with the field reality, and improved significantly the prediction accuracy of the mapping, which fully reflected the variation of soil organic matter in the black soil region of Jilin Province.