The Horqin Sandy Land, a critical ecological barrier in northern China and the core area of the Three-North Shelterbelt Project, requires precise monitoring of aeolian desertification (AD) dynamics to maintain ecological stability in densely populated northern and northeastern China. This study innovatively integrated multisource remote sensing parameters via Google Earth Engine to systematically compare three feature space models (Albedo-NDVI, Albedo-MSAVI, and Albedo-KNDVI) and five machine learning algorithms for AD monitoring, while applying the Optimal Parameter Geographic Detector (OPGD) to quantify natural-anthropogenic driving mechanisms. High-resolution AD classification (5-year intervals, 2000-2020) revealed spatiotemporal dynamics, with machine learning models consistently outperforming feature space models. Random forest achieved the highest accuracy (97.7%) and Kappa coefficient (0.967), effectively distinguishing nondesertified areas from extreme desertification. Temporal analysis showed a significant AD reversal: total desertified area declined to 30 166.35 km(2) by 2020, driven by ecological policies and climate amelioration. OPGD analysis indicated soil texture as the strongest single driver (q > 0.48), but factor interactions (e.g., SD boolean AND & gnE; LU) exerted greater explanatory power, highlighting compound effects of natural and anthropogenic factors. This study provides a robust framework for high-precision AD monitoring in semiarid agropastoral zones, supporting global efforts toward zero land degradation.