【Objective】 Soil organic matter (SOM) content is an important soil index, essential to guiding usage of chemicals in agriculture, and also an important factor affecting regional carbon balance. Scholars have long been interested in the study of soil organic matter and have helped address key environmental, agricultural and social and political issues over the past ten years. It is essential to have simpler, more accurate, more rapid and more inexpensive methods for plotting soil organic matter maps, and moreover, more time-and-cost saving ones.【Method】 Remote sensing data have extensively been used in digital soil mapping, especially in assessing soil organic matter, because the use improves accuracy of the prediction of soil physical parameters to some extent. To explore feasibility of combining the narrow band of hyperspectrum and the wide band of multispectral remote sensing images to realize high-accuracy prediction of soil organic matter (SOM), field data and soil samples were collected in Weigan River Oasis of Kuche in May of 2016 for in-lab analysis of SOM content using the potassium dichromate method; hyperspectral data were in the darkroom with the aid of the ASD Fieldspec3 spectrometer; the first 7 bands of the Landsat 8 OLI remote sensing images of May 30, 2016 were selected and used for atmospheric correction, radiometric correction and fine geometric correction of the images as pretreatment. In addition, the band averaging method was used to fit the measured data of the hyperspectral narrow bands into data of the multispectral wide bands, and then to screen out sensitive spectral parameters; models for assessing soil organic matter were built up, using the partial least squares regression method for test and screening of an optimal model. In the end, spatial distribution of soil organic matter was analyzed, taking into account all soil factors.【Result】Results show that SOM content in the oasis varies in the range of 3.57 ~ 39.22g kg^-1. An optimal prediction model was built up based on the 2nd, 5th and 6th bands as independent variables after being subjected to first differential transformation, with determination coefficient R² of the model dataset being 0.852 and of the validation set being 0.897. On such a basis, the optimal model was applied to multi-spectaral data based prediction of soil organic matter using the Landsat 8 OLI satellite images. Differential transformation significantly improved the correlation of hyperspectrum with soil organic matter content. After the reflectances of the multi-spectral bands were calibrated with the ratio method, determination coefficient R² of the validation dataset was raised from 0.711 to 0.849. Distribution of soil organic matter was less affected by land use types or soil texture than soil particle composition.【Conclusion】The inversion of SOM indicates that the remote sensing based inversion of SOM fits the actual situation of the study area, displaying good reliability and authenticity. In this study, the findings are the same as and different in places from those of other scholars, so further studies should take into account effects of soil moisture content, salinity, landform and some other factors on soil organic matter content and improve accuracy of the prediction model. All the findings of the study exploring feasibility of combining the hyper-spectral model with remote sensing inversion in predicting soil organic matter in the studied area may serve as scientific basis and technical reference for quick acquisition of SOM information in Arid and semi-arid regions.