## 1 Introduction  
Grasslands, constituting approximately 20% of the world's terrestrial area, represent one of the most extensive global ecosystems [1]. These ecosystems possess dual economic and natural attributes [2]. However, exacerbated by overgrazing and climate change, grassland desertification has become increasingly severe [3]. Through persistent restoration efforts, vegetation diversity and coverage in desertified grassland areas have significantly improved, highlighting the critical need for robust monitoring of vegetation growth dynamics and coverage changes. Remote sensing technology has emerged as a powerful tool for grassland ecological monitoring due to its large-scale, rapid, and accurate acquisition of high spatio-temporal-resolution vegetation change information [4]. Compared to traditional methods, remote sensing offers superior spatial advantages, serving not only as a primary means for regional vegetation coverage assessment but also enabling global-scale estimations. Numerous vegetation indices have been developed, among which the Normalized Difference Vegetation Index (_NDVI_), proposed by [PERSON] et al. in 1974 [5], remains one of the most widely used indicators for monitoring global vegetation dynamics [6; 7; 8]. _NDVI_ effectively captures spatial variations in vegetation across major climate zones and serves as a reliable proxy for surface vegetation coverage and growth status. To further enhance monitoring accuracy, [PERSON] et al. [9] introduced a nonlinear generalization of _NDVI_. In 2021, [PERSON] team proposed the kernel Normalized Difference Vegetation Index (_kNDVI_), demonstrating its superior precision through comparative experiments with _NDVI_ and _NIRv_. Subsequent studies by [PERSON] et al. [10] validated _kNDVI_'s robust performance in vegetation monitoring and emphasized its utility for terrestrial ecosystem assessments. [PERSON] et al. [11] developed a comprehensive _kNDVI_ dataset to characterize spatio-temporal vegetation coverage changes and climate-driven factors in the Yellow River Basin from 2001 to 2020. Unlike conventional RGB imagery, remote sensing data contain multiple spectral bands, offering richer information for analysis. Training semantic segmentation models requires large volumes of high-quality labeled data, with quantity and quality directly influencing model stability and adaptability [12]. For instance, [PERSON] et al. [13] successfully extracted urban green space distribution from GF-2 imagery using DeepLabV3+ semantic segmentation networks. Comparative studies by [PERSON] et al. [14] demonstrated that U-Net-based semantic segmentation outperformed random forests in Landsat multi-spectral land cover classification, achieving higher overall accuracy and F1 scores. Similarly, [PERSON] et al. [15] integrated artificial intelligence and deep learning for advanced vegetation canopy assessment and monitoring. With the development of artificial intelligence technology, deep learning algorithm has been gradually applied to the field of vegetation change monitoring. However, existing studies still face challenges in accurately capturing vegetation dynamics in complex desertified grasslands. Given the limitations of traditional _NDVI_ in sparse vegetation areas and the lack of deep learning models for grassland desertification monitoring, the core research questions of this study are  
1. How can the kernel Normalized Difference Vegetation Index (_kNDVI_) improve the accuracy of vegetation coverage estimation in desertified grasslands? 2. Can a deep learning model integrating spatio-temporal features and _kNDVI_ (_kNDVI_-PT-Former) enhance the precision of vegetation change monitoring in complex ecological restoration areas? 3. What are the spatio-temporal dynamics of vegetation coverage in Wuzhumuqin Sandy Land from 2019 to 2024, and how do they reflect the effectiveness of ecological restoration?