Earth, and the interval between repeated experiments is too long [10; 11; 12]. Therefore, more sophisticated remote sensing equipment needs to be deployed or used to achieve a finer classification of desert grassland vegetation. In recent years, with the continuous development of unmanned aerial vehicle (UAV) technology, it has become well-known to the general public for its simple operation methods, low cost of use, and access to areas that are difficult for humans to reach [13]. Advances in optical technology have led to the development of portable hyperspectral imagers that offer higher spatial and spectral resolutions and a more prosperous continuous spectral band than satellite remote sensing. This provides a higher recognition accuracy for delineating fine features. In contrast to traditional RGB color images, hyperspectral images can unveil more hidden features within invisible bands, which are crucial for the classification and monitoring of desert grassland plants. The capability of hyperspectral imaging to intricately distinguish and capture the spectral properties of matter in minute detail renders it a powerful tool in fields such as ecology, agriculture, and environmental science. Using UAVs as platforms to carry portable hyperspectral imagers, the two can complement each other to build low-altitude UAV remote-sensing platforms [14; 15; 16; 17]. This platform is now widely used in vegetation cover calculations [18], agricultural precision management [19], vegetation leaf area monitoring [20], and vegetation condition monitoring [21; 22], among other applications. In hyperspectral remote sensing image processing, the vegetation index method is commonly used to calculate the numerical indicators of the reflectance or radiation of features in remotely sensed images. It is used to assess the growth status of objects and vegetation cover and to monitor vegetation changes on the land surface [23; 24; 25; 26]. These vegetation indices are dimensionless values [27]. By conducting vegetation index calculations on hyperspectral images, the most appropriate separability threshold for each feature is determined based on the calculation results, thereby completing the task of classifying image features. The most widely used vegetation indices include the Normalized Difference Vegetation Index (NDVI) [28], Ratio Vegetation Index (RVI) [29], Difference Vegetation Index (DVI) [30], and Soil-Adjusted Vegetation Index (SAVI) [31], among others. Researchers have improved commonly used vegetation indices and explored several practical applications. Ref. [32] studied the leaf area index of winter wheat in arid areas and used first- and second-order differential data preprocessing to construct two-dimensional and three-dimensional vegetation indices by combining arbitrary wavebands. The results showed that the correlation between vegetation and leaf area indices formed by combining wavebands was significantly improved. [PERSON]. [33] constructed a microplaque index threshold (MPI-T) for the problem in which NDVI and SAVI are difficult to distinguish between desert grassland rat holes and achieved positive recognition results. However, the vegetation index calculation method has limitations and cannot fully exploit the rich waveband information in hyperspectral image data. With the emergence of big data and advancements in computer technology, machine and deep learning techniques have rapidly developed. Researchers have widely applied these techniques to grassland monitoring and classification. In a study on desert grasslands, ref. [34] achieved an overall classification accuracy of 91.06% using the random forest algorithm to classify grassland vegetation. However, in hyperspectral images, machine learning methods for image classification require the manual extraction and analysis of image features, which is time-consuming and labor-intensive. In deep learning, convolutional neural networks (CNNs) are among the most widely used and representative algorithms.