Abstract: This study conducted the inversion of the organic matter content in the soil of the black soil area in Sunwu County, Heilongjiang Province using the Sentinel-2A multispectral remote sensing images and the surveyed soil data. After preprocessing the images, the characteristic bands were selected through correlation analysis and using the random forest (RF) method. Subsequently, a multispectral inversion model for the organic matter content of the soil was built using the partial least square method and the BP neural network, and the inversion of the organic matter content of the soil in the Hongqi Forest Farm was conducted. According to the obtained results, the bands selected based on the reciprocal of the logarithm of the first-order differential of reflectance through the correlation analysis and the combined bands selected using the RF method can effectively improve the inversion precision of the organic matter content in the soil, and the RF-BP neural network model for the combined bands yielded the optimal inversion performance (R2=0.7245 and RMSE=1.3127%). The results of this study will provide technical support and reference for the dynamic monitoring of the organic matter content in soils.