## 1 Introduction
proposed that the different vegetation types have a different maximum light energy utilization and developed a regional NPP estimation model using the Inner Mongolia vegetation type as an example. The regional NPP estimation model estimated vegetation net productivity and analyzed its spatial and temporal distribution characteristics [27]. However, the majority of previous studies used MODIS-NDVI or GIMMS-NDVI models to analyze desertification by calculating vegetation cover, while a few studies used an Albedo-NDVI feature space approach for desertification in the Urat front flag. However, the research data are outdated and unrepresentative. The majority of research focuses on the influencing factors of desertification, desertification evaluation indexes, and the monitoring and comparison of desertification degrees in different periods. There are insufficient studies on the rapid positioning, identification, and spatial distribution of desert areas formed by the land degradation process under long time series, as well as the problems of strong subjectivity of research results, difficulty in realization, low precision, and complexity of methods. Desertification appears as a bare land surface in remote sensing images. The enhancement of information and the weakening of vegetation information can be characterized by index factors such as surface albedo, surface temperature, surface humidity, vegetation index, and vegetation cover. The normalized vegetation index (NDVI) is a crucial biophysical parameter reflecting the state of surface vegetation, whereas the surface albedo is a physical parameter representing the reflection characteristics of the surface to solar short-wave radiation. As desertification increases, surface vegetation is severely damaged, surface vegetation cover decreases, biomass drops, and surface roughness increases, which is reflected in remote sensing images by a corresponding decrease in NDVI value and an increase in surface albedo. Therefore, it is highly representative to research and analyze the degree of desertification in the region using \"Albedo-NDVI\" feature space. The dry climate, scarcity of precipitation, temperate continental arid climate, and excessive impact of human activities on the ecological environment have resulted in the ecological environment of the Urat front flag facing problems with soil salinization, sanding, and pasture degradation, weakening the ecological environment [28; 29]. According to previous desertification research, combining the vegetation index and surface albedo by constructing an \"Albedo-vegetation index (NDVI) feature space\" enables more effective and convenient quantitative monitoring and research of the spatial and temporal distribution and dynamics of desertification [30]. In addition, it can serve as a theoretical basis for ecological restoration and environmental management in the region. In light of this, to analyze the spatial and temporal characteristics and evolution patterns of land desertification in the Urat front flag, this study created the region's \"Albedo-NDVI\" feature space between 2010 and 2020 using the ENVI and ArcGIS software platforms. Additionally, using Landsat remote sensing images as the primary data source, as well as dynamic attitude calculation [31], transfer matrix analysis [32], and center of gravity shift analysis [33], and in conjunction with MODIS-NDVI data, the CASA model was used to estimate the net primary productivity of vegetation between 2010 and 2020 [27; 34]. To accurately and objectively evaluate the ecological environment of the study area, this paper analyzes the spatial distribution pattern and the impact of topographic factors on desertification. It also provides a theoretical basis for the ecological environment governance of the Urat front banner, and serves as a reference for the ecological environment protection and rational development of Hetao oasis and arid and semi-arid areas.