In this paper, according to the feasibility and reliability of using the hyperspectral data to retrieve SOM from hyperspectral data, combined with the high efficiency of differential processing in extracting spectral information, a new method based on differential algorithm for soil organic matter modeling In this study, the content of soil organic matter can be obtained by differentiating the multi-spectral remote sensing images directly, which aims to provide the direction for the future study of soil organic matter rapid measurement is proposed. In this paper, Landsat 8_OLI multi-spectral remote sensing image data is used to perform the radiation calibration, geometric correction, atmospheric correction, mosaic and cropping of multi-spectral remote sensing images. The first order differential and second order differential are processed by IDL software. The image can better express the real situation of the object. The first-order differential image can distinguish the water body from the soil better. The original remote sensing image contains a lot of information, including the noise. The differential image processed by the remote sensing image excludes the original image In the study area, five-point method was used to collect soil samples, indoor potassium dichromate oxidation-volume method to measure soil organic matter data, and multispectral data was used to analyze soil organic matter data from the ground to analyze soil organic matter It is found that there is a sensitive band in the correlation between the first-order differential data and soil organic matter content, indicating that the first-order differential processing can transform the original remote sensing image data in some obscure soil in the multi-spectral range. Organic information is released; select a high correlation number established based on the raw remote sensing data, first-order differential data, single-band multi-spectral data of the second order differential linear and multi-band multi-spectral linear model, and select the best model to estimate soil organic matter content retrieval. The main conclusions are as follows: (1) By differentiating the original image, it is found that the image after differential processing changes obviously and the image noise of first-order differential processing decreases, which further highlights the hidden information of soil organic matter in the image. The second-order differential processing suppresses soil organic matter information. (2) The data of the original remote sensing images have a low correlation with soil organic matter content. The data of the first-order differential treatment reflect the correlation of the soil organic matter sensitive band, that is, the partial band data, and the second-order differential processing after the remote sensing images of each band data on soil organic matter content of the correlation is weak. (3) Multi-band modeling is superior to single-band modeling, and the first-order differential multiband model has the best prediction accuracy. The model’s coefficient of determination and the coefficient of model fitting are 0. 898 and 0. 854 respectively. The soil organic matter content in this region was well estimated. The fitting accuracy of single-band model and multi-band model was compared comprehensively. It was found that both the single-band model and the first-order differential model had better prediction ability. (4) Based on the first-order differential multi-band model, the inversion of SOM in the study area was carried out. The inversion result is in accordance with the actual situation, which provides a practical method and reference for the mapping of soil organic matter content in arid area.