The performance of the dual-temporal spectral index (the band combination of two images) in predicting soil organic matter (SOM) was investigated over a double-cropping agricultural region (Fengqiu County) in the Huang-Huai-Hai Plain, where the bare soil period is often short for remote sensing of soils. In the study, a total of 117 soil samples were collected and dual-temporal Landsat 8 satellite images during the bare soil period (Acquisition date: October 6, 2014 and October 30, 2017) were selected for establishing four types of spectral indices: ratio spectral index, difference spectral index, normalized spectral index and optimized spectral index. Then, these indices were used as the input in SVM (Support Vector Machine) models of SOM after being selected by the variable selection method of LASSO (Least Absolute Shrinkage and Selection Operator). The results of leave-one-out cross-validation showed that, compared with image bands or spectral indices built by single images (single-temporal spectral index), the dual-temporal spectral index could make better use of temporal information of images and its prediction accuracy was higher for SOM (R²=0.53, RMSE=2.01g/kg). Moreover, the spatial distribution pattern of SOM predicted by the dual-temporal spectral index was consistent with the real condition. Thus, the proposed method of using the dual-temporal spectral index for SOM prediction in the study could promote prediction and mapping of soil properties in areas with short bare soil periods.