As grassland degradation and desertification are becoming more serious in the northern Tibetan plateau, estimating gravel grain size is important for desertification evaluation and dynamic monitoring. Based on hyperspectral remote sensing technology, this paper combines ground survey, GPS positioning, gravel spectroscopy and gravel grain size determination, preferably selecting the waveband with the highest correlation with gravel grain size, and establishes a linear fitting model between gravel grain size and sensitive waveband, and extracts the spatial distribution characteristics of gravel grain size in the experimental area using hyperspectral images of HMS-5. The results show that: the bands with better correlation are at 369.9, 371.5 and 910.5 nm, where the first-order derivative at 910.5 nm has the best fitting effect with gravel grain diameter (R2=0.738); the fitting comparison of different spectral absorption parameters with gravel grain diameter, the fitting accuracy of the fitted regression model established by the absorption area near 2 340 nm and the gravel grain diameter is The fitting accuracy of the fitted regression model is relatively high (R2=0.728); in the inversion of gravel grain size by spatial remote sensing, the accuracy reaches 70%, as well as briefly analyzing the spatial distribution characteristics of gravel grain size, which provides a reference basis for the desertification analysis of the northern Tibetan plateau.