CNNs consist of a convolutional layer for feature extraction and a sampling layer for feature processing, an \"end-to-end\" learning approach that distinguishes machine learning from other algorithms [35]. Ref. [36] used a multilayer feature fusion 2D convolutional neural network (MFF-2 DCNN) to identify micropatches on the surface of desert grasslands, achieving a high classification accuracy for rat holes and bare soil. However, a 2D-CNN cannot capture spectral information effectively in hyperspectral image information extraction tasks, destroying the 3D structure of the image data. To address this issue, some researchers have applied three-dimensional convolution (3D-CNN) to hyperspectral images to address this issue. Ref. [37] classified the vegetation and bare soil in desert grasslands by constructing a 3D-CNN model and continuously optimizing it. The network models developed in these studies have shown promising results in classifying desert grassland features. However, these models did not consider memory consumption, which could pose considerable challenges for future deployment on mobile devices and the rapid monitoring of desert grassland degradation. Currently, there is no sufficiently detailed method for selecting hyperspectral image-band data for desert grasslands. To address the issue of data redundancy, Principal Component Analysis (PCA) is often used to reduce the dimensionality of the image data. However, this can result in the reorganization of the original image data features [38; 39; 40], or the direct discarding of bands with substantial fluctuations in the spectral curves of features owing to undesirable noise [41]. Therefore, optimal bands cannot be selected to simplify hyperspectral data, which presents challenges for subsequent data processing. Additionally, 3D convolutional operations are computationally demanding and involve numerous training parameters, exacerbating these problems. Therefore, there is an urgent need for methods that enable data dimensionality reduction and the construction of lightweight network models to achieve efficient and accurate grassland monitoring. To solve these problems, this study used a UAV hyperspectral remote sensing system to collect hyperspectral data on vegetation in a desert grassland in the Inner Mongolia Autonomous Region. A convolutional neural network model was proposed based on feature enhancement, which was applied to vegetation plant taxa classification. The most accurate vegetation species classification model was obtained through data, model, and parameter optimization. This study aimed to provide a new method for achieving the efficient and high-precision dynamic monitoring of desert grassland species by constructing a streamlined 2D-CNN classification model. The main contributions of this study were as follows:  
1. Based on an improved depth-separable convolution to improve the nonlinear fitting ability of the model, this study proposed a streamlined 2D-CNN (SL-CNN) model for desert grassland plant taxa classification. This model effectively explored lightweight convolution in desert grassland species classification research and could achieve the efficient and high-precision monitoring of grassland species. 2. The model used improved convolutional block attention (CBAM-F) to effectively focus on important channel features and key spatial information and improved the model's feature refinement capability by adaptively learning feature map channels and spatial relationships. It was combined with residual block convolution (RBC-F) to fuse the feature data and improve the model classification performance. 3. Using the variance and Frobenius norm\({}^{2}\) feature band selection methods, we could efficiently reduce the dimensionality of the data, enhance the computational efficiency of the model, retain important information for classification tasks, and effectively alleviate data redundancy in hyperspectral images.