The research on high-precision space-based monitoring of soil organic carbon content is one of the important research in the fields of agricultural remote sensing and environmental remote sensing, which is of great significance for the utilization and protection of soil resources and the development of agricultural work. The spectral channel, Spectral resolution, signal to noise ratio and other core parameters of spaceborne hyperspectral instruments directly affect the accuracy of quantitative inversion and prediction of soil organic carbon. This study carried out the research on the impact of satellite load Spectral resolution, signal to noise ratio, and spectral characteristic bands on the retrieval of organic carbon in different black soil type, and proposed a model based on atmospheric transmission model, Spectral resolution analysis model, signal to noise ratio analysis model The full link simulation analysis method of hyperspectral satellite "ground- atmosphere-instrument-observation-inversion" for different types of soil monitoring based on the extraction and analysis model of characteristic bands and Partial least squares regression inversion model realizes the coupling effect analysis of Soil type, atmospheric effect, instrument characteristic parameters and inversion methods. The results show that: (1) The best Spectral resolution of soil organic carbon inversion in different types of black soil is in the range of 10nm-20nm. (2) Different Soil type have different requirements for the observed signal-to-noise ratio. For organic Carbon monitoring of Phaeozem, there is a higher signal to noise ratio requirement than the other two soils. (3) The optimal Spectral resolution and signal-to-noise ratio required under different feature band extraction and analysis methods are consistent. Different types of soil spectral data extract different characteristic bands. The Soil type with the best inversion effect is Chernozem, with 26 characteristic bands, ,. (4) The inversion model has no coupling relationship with the instrument characteristic parameters, and the best Spectral resolution and signal noise ratio requirements of different inversion algorithms for the same type of soil are consistent. (5) The best inversion parameters of Chernozem SOC are spectral resolution 15nm, signal noise ratio greater than 506.66, and the number of feature bands extracted is 26; The optimal inversion parameters of Kastanozem SOC are spectral resolution 17nm, signal noise ratio greater than 331.42, and the number of feature bands extracted is 22; The requirements for the best inversion parameters of Phaeozem SOC are that the spectral resolution is 15nm, the signal-to-noise ratio is greater than 432.51, and the number of feature bands is 19. At the same time, the black soil samples from San Jiang Plain black soil area in China are used to verify that the above model results are still valid. This study has guiding significance for the design of space-based soil remote sensing instruments.