Situated as a critical ecological barrier in northern China, the Mu Us Sandy Land faces escalating desertification, progressive vegetation loss, and intensified anthropogenic disturbances, predominantly driven by recurring drought events and the expansion of agricultural land. Understanding the spatiotemporal dynamics and underlying drivers of ecological environment quality is vital for informed ecosystem management and restoration planning. In this study, we utilized the Google Earth Engine cloud platform and multi-source remote sensing data to calculate the Remote Sensing-based Ecological Index (RSEI) for the Mu Us Sandy Land from 2000 to 2024. We investigated multiple-dimensional change in environmental quality and applied the Optimal Parameters-based Geographic Detector model to quantify the independent and interactive contributions of natural and anthropogenic drivers. Results showed: (1) Ecological environment quality exhibited a significant upward trend, with the average RSEI increasing from 0.26 to 0.35 (average annual rate = 0.002, p < 0.05), and was spatially higher in the southeast than in the northwest. (2) Areas with "fair" ecosystem quality predominated, comprising 60.1% of the region annually. Improvement zones (67.5%) were mainly located in the northeast and south, aligned with large-scale ecological restoration efforts, while degraded areas (32.4%) were associated with dune encroachment, vegetation decline, and urban expansion. (3) Human activities were the dominant drivers of ecological change, with afforestation area (q = 0.34), large livestock numbers (q = 0.32), and GDP (q = 0.27) exerting the greatest influence. Significant interactions effects, particularly those between GDP, population density, and other natural or anthropogenic drivers such as wind speed and livestock numbers - exerted a markedly strong influence on ecological restoration effects in the Mu Us Sandy Land. This study provides a systematic approach integrating remote sensing and spatial analysis, offering critical insights for sustainable land management in arid and semi-arid ecosystems.