Abstract: Soil organic matter (SOM) refers to the general term of all kinds of carbon (C)-containing organic compounds in soil. Its dynamic change not only affects the stability of agricultural ecosystem, but also is closely related to the C cycle of atmosphere and biosphere. It is of great significance to the large-scale monitoring of soil organic C content and C storage. In this study, 145 soil samples were collected from the field of the Three-Rivers Source region in July 2017 and 2018 to detect the spectral information of soil. Then, the correlations between original spectral reflectance data and the spectrum under different data transformation forms of SOM were carried out, and the characteristic bands were selected. In addition, the partial least square regression (PLSR), support vector machine (SVM) and random forest (RF) models were used to simulate and estimate the content of SOM in the Three-Rivers Source region. The results showed that the content of SOM was significantly different among different soil depths, and showed a downward trend with soil depths. The test accuracy of the three modeling methods was decreased in the order of RF > SVM > PLSR. The combined model of RF and FD (the first-order differential) showed the best simulation accuracy (R² and RMSE of modeling set were 0.9678 and 8.9132 and those of verification set were 0.7841 and 20.9787, respectively). For the inversion of soil organic matter content in the three river source, the best data transformation methods of different models are different. The results of this study could provide a theoretical support for the subsequent hyperspectral remote sensing inversion, so as to realize the rapid detection and real-time dynamic monitoring of SOM content in the Three-Rivers Source region.