Comparing the soil organic matter (SOM) prediction model based on the broad and narrow bands and the difference in spatial pattern distribution, we validate the feasibility of using satellite remote sensing data to monitor soil basic ecological parameters by ground hyperspectral measurement and analysis of soil. Taking the soil of Tianshan as the research object, we calculate comprehensive spectral index of the broad and narrow band, respectively, using correlation analysis and principal component analysis in the organic matter of unmanned interference area, human interference area, and choosing the comprehensive spectral index with better correlation coefficient and characteristic vector value as the independent variables, using multivariate linear regression model (MLR) and partial least squares regression model (PLSR) to establish respectively the hyperspectral prediction model of SOM in broad and narrow band of unmanned interference area and human interference area. The validation, the comparison and selection the model are carried out. Finally, we analyze and inverse the spatial pattern of SOM content based on the best model of research area. Results show that, through the correlation analysis and principal component analysis of the organic matter and salinity index, vegetation index to establish the MLR and PLSR of organic matter component, we pick out the salinity index 2 (SI2), salinity index 3 (SI3) and ratio vegetation index (RVI), normalized difference vegetation index (NDVI) of narrow band, and the SI1, SI2, RVI, NDVI of broad band in unmanned interference area; we pick out SI1, SI3, RVI and NDVI of narrow band, and SI1, SI2, RVI and renormalized difference vegetation index (RDVI) of broad band. Taking these parameters as independent variables, we build MLR and PLSR models of soil organic matter. By comparing the precision of the models, we find that the PLSR model with narrow band has high precision in human or unmanned interference areas, and determinable coefficient and relative percent deviation are 0.753, 2.01 and 0.819 and 2.14, respectively. Spatial inversion and analysis of SOM in research area are carried out based on the best model above. The mass fraction of organic matter in unmanned interference area is concentrated in less than 10×10-3, and presents the trend of low in middle and high in around. The mass fraction of organic matter in human interference is 10×10-3-15×10-3, presents the trend of low in southwest and northeast, and high around middle north region.