The desertification of Gansu Hexi Corridor is serious, which seriously affects the environment of living and production activities of local residents. Hyperspectral remote sensing technology is an important research method for desertification land degradation degree, land type identification and remote sensing inversion. This paper takes desertification land in Hexi area as the study object. The object was analyzed for the relationship between its spectral characteristics and vegetation degradation degree, plant type, season and soil type, and the spectral characteristics of desertified land in Hexi region were discussed. The main results are as follows; (1) When the vegetation coverage is less than 20%, the vegetation degradation has little effect on the sand spectrum in different degradation stages of the same vegetation type, and the spectral reflectance of the sand is close to the bare land, especially when the vegetation coverage is less than 10%, the spectral curves of sand and bare land almost coincide, and it is difficult to reflect the degree of desertification of the land only from the vegetation landscape. (2) Different vegetation types have a certain influence on the reflectivity of sandy land spectrum. The spectral reflectance of sandy land with Nitraria tangutorum Bohr as constructive species is higher, followed by Haloxylon ammodendron sandy land, and the Tamarix ramosissim sandy land are relatively low. The sandy spectrum of the indicator plants under different succession stages of vegetation can reflect the desertification process of the land. (3) During the plant growing season, the spectral reflectance of sandy land is affected by soil, plant water content and plant phenology, which is higher than other months from August to October and lowest in July. The seasonal variation of the spectral curve of sandy land can reflect the changes in soil water content in sandy land. The research results provide a research basis for the determination of land desertification degree, seasonal information extraction and vegetation coverage estimation in remote sensing monitoring of desertification land.
