Accurate quantification of aboveground biomass (AGB) for sparse desert vegetation is fundamental for assessing regional carbon sink potential, yet large-scale data retrieval remains constrained by high spatial heterogeneity and severe background noise. Focusing on Haloxylon ammodendron communities in the Gurbantunggut Desert, this study integrated plot-level ground truth derived from Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) with multi-source satellite imagery (Sentinel-2 and Jilin-1) to evaluate AGB estimation accuracy and spatial distribution patterns across various feature combinations and employed four machine learning algorithms at a 10 m pixel scale. The Difference Vegetation Index (DVI) exhibited the strongest explanatory power for AGB spatial variance, whereas downsampled high-resolution textures induced feature redundancy. Among the evaluated algorithms, the Random Forest (RF) model driven solely by multispectral parameters achieved the optimal cross-scale mapping accuracy (R² = 0.72, RMSE = 1.32 t ha⁻¹). The total regional AGB storage was estimated to be approximately 6.99 × 10⁴ t, with low-density habitats (0.2–2.0 t ha⁻¹) occupying 90.39% of the area. This study confirms the feasibility of integrating UAV point clouds with multi-source satellite imagery for the large-scale retrieval of sparse shrub biomass, providing a quantitative basis for desert carbon management.