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Grasslands support vital ecosystem services but face growing threats from climate change and human pressures. Earth observations offer useful information for monitoring vegetation degradation across broad scales, yet traditional ground-to-satellite models often suffer from plot-to-pixel mismatches and limited field coverage. To address these challenges, we developed a two-step remote sensing framework in the central Mongolian steppe that links field measurements to unoccupied aerial vehicle (UAV) data and UAV data to Sentinel-2 imagery. Our study asked: (1) to what extent UAV data can accurately predict vegetation structure (cover and height), (2) whether a two-step modeling approach improves landscape-scale prediction accuracy relative to single-step ground-to-satellite models, and (3) how applicable the two-step approach remains when transferred across space or time. UAV models predicted vegetation cover and height with mean R 2 values of 0.64 and 0.56, respectively. Incorporating UAV-derived predictions into satellite models substantially improved performance, with R 2 values of 0.90 for cover and 0.82 for height. Stratified cross-validation (CV) produced lower error estimates but applied to only ∼60% of the study area based on the Area of Applicability (AOA). In contrast, spatial leave-site-outs (LSO) CV produced higher errors but remained applicable to ∼90% of the region and was stable across years. These results show that the clustered nature of two-step training data makes LSO CV a more rigorous and representative evaluation strategy. More broadly, our framework demonstrates how multiscale validation and AOA-based assessment support transferable models capable of detecting emerging patterns of vegetation structural change to inform timely, targeted conservation actions. • UAV surveys can provide large extents of estimated grassland structure • Integrating UAV data drastically improves satellite scale models • Spatial CV may provide a more robust estimate of errors for multiscale studies