## 5 Conclusions  
The classification of desert grassland taxa is essential for studying the process of grassland desertification. However, this study has some limitations. In this study, we built a UAV hyperspectral remote sensing system to collect remote sensing images of desert grassland vegetation efficiently and precisely under natural light to compensate for the shortcomings of traditional grassland survey methods. We developed a lightweight 2D-CNN model called SL-CNN for classifying desert grassland taxa. We used an improved depth-separable convolution to ensure species classification accuracy and achieve convenient and rapid species monitoring. To prevent information redundancy in the hyperspectral data, we used a combination of variance and F-norm\({}^{2}\) operations for feature band selection. We constructed a CBAM-F feature refinement module by improving the channel attention in the CBAM attention module. This was combined with the RBC-F residual block feature enhancement module to improve the feature extraction capability and classification performance of the network model.  
In this study, four important parameters of the model were optimized, the effects of different parameter values on the classification performance of the model were analyzed, and ablation experiments were conducted to verify the effectiveness of the building blocks. To demonstrate the advantages of the model, it was compared with the latest and most commonly used hyperspectral image classification models. The results showed that the OA, AA, and Kappa values of this model performed more effectively than those of the other models, with 99.216%, 98.442%, and 98.735%, respectively. It had the advantages of fewer parameters, relatively fast construction, and lower memory occupation. This study has provided a new research method for monitoring the degradation of desert grassland features using UAV remote sensing technology.  
However, desert grassland features are usually small and sparse, and the phenomenon of \"same thing, different spectrum\" and \"same spectrum, different thing\" often occurs in remote sensing images, which poses great difficulties in data annotation. Therefore, future research should address the effective classification and inversion of features using a small number of samples. In addition, the SL-CNN model needs to be further optimized to reduce its construction time and memory footprint for subsequent deployment in mobile terminals. This provides additional potential for practical applications.  
Conceptualization, S.W.; methodology, S.W. and Y.B.; software, S.W.; validation, S.W. and Y.B.; formal analysis, S.W.; investigation, T.Z.; resources, X.G.; data curation, E.J.; writing--original draft preparation, S.W.; writing--review and editing, Y.B.; visualization, T.Z.; supervision, J.D.; project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results received funding from the National Natural Science Foundation of China (Grant No. 31660137), the Research Key Project at Universities of Inner Mongolia Autonomous Region (Grant No. NJZZ23037) and Inner Mongolia Autonomous Region Natural Science Foundation Joint Fund Project (Grant No. 2023 LHMS06010).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data privacy.  
The authors declare no conflict of interest.