Due to over-exploitation by humans and global climate change, desertification has become an increasingly severe issue, seriously threatening the stability of ecosystems and the sustainable development of resources. Therefore, this study focuses on the Hangjin Banner region in Inner Mongolia, using satellite remote sensing and remote aerial vehicles (RAV) remote sensing technology. Through wide-area coverage, long-term monitoring, multiscale analysis, and high-precision interpretation, the study demonstrates the strong synergistic effects of multiscale interpretation and data fusion applications, systematically carrying out desertification monitoring grading and refined vegetation extraction. First, to address the problem that the information dimension of a single index is insufficient and it is difficult to reflect the development trend of desertification, the normalized difference vegetation index (NDVI)-albedo feature space applicable to the desert environment is inversely performed based on Landsat 8 satellite images from 2009 to 2023. Then, on the basis of the feature space, the desertification difference index (DDI), which realizes the wide-area desertification monitoring grading and spatio-temporal evolution analysis of the study area, and the hue-saturation-lightness greenway enhanced vegetation index (HSLGEVI), which has stronger applicability and stability in desert environments, were constructed based on the HSL color space and the hue tuning algorithm. This index can effectively overcome the limitations of the RGB vegetation index, clearly delineate the canopy edge of desert vegetation, and accurately extract surface meadow vegetation with lower chlorophyll content. To test the effectiveness of the HSLGEVI, the widely used and validated excess green index (EXG), vegetation difference vegetation index (VDVI), modified green-red vegetation index (MGRVI), and red-green-blue vegetation index (RGBVI) were selected for comparison. The results show that the accuracy of HSLGEVI is better than that of other indices, with overall accuracy and ${F}1$ -score remaining above 90%. It reduces the impact of the RGB color space vegetation index on the accuracy of vegetation extraction, effectively overcoming misclassification and omission issues, and providing a reliable monitoring mechanism for desertification control in the Hangjin Banner area.