A multi-tiered representativeness framework mitigating spatial scale effects in FVC validation: UAV-based assessment of global products in Qinghai-Tibetan Plateau alpine grasslands

• Multi-tiered framework mitigates spatial scale effects in FVC validation accuracy. • The proposed framework reduces validation uncertainty by 40% via UAV-ground synergy. • Persistent biases in global FVC products identified for alpine grasslands. • Decadal UAV network enables robust validation across Qinghai-Tibetan Plateau. Ground-measured fractional vegetation cover (FVC) data are critical for validating satellite-derived FVC products. However, spatial scale mismatches between ground plots and satellite pixels, compounded by the scarcity of field data in remote regions, introduce significant uncertainties in product validation. This study proposes a novel multi-tiered representativeness framework integrating three key indices: the absolute difference of spatial upscaling transformations, the heterogeneity of the surrounding environment, and FVC temporal stability. The framework’s implementation leveraged an unmanned aerial vehicle (UAV) observation network (870 monitoring plots, 2015–2024) within the alpine grassland ecosystem of the Three-River Source Region on the Qinghai-Tibet Plateau, classified into four levels, with levels 1–2 indicating high level and 3–4 lower. Our results reveal that both the environmental heterogeneity of monitoring plots and scale mismatches substantially impact the validation accuracy of FVC products. By applying the proposed framework, validation using high-level monitoring plots reduced uncertainty by approximately 40 % compared to using all monitoring plots (GEOV3: R 2 = 0.964, RMSE = 0.075 vs. R 2 = 0.830, RMSE = 0.138; GLASS: R 2 = 0.957, RMSE = 0.068 vs. R 2 = 0.812, RMSE = 0.121), highlighting its effectiveness in mitigating spatial representativeness errors. Furthermore, the validation results for two global FVC products (GEOV3 and GLASS) highlight systematic biases in their performance within alpine ecosystems. These findings advance validation methodologies for remote sensing products in heterogeneous landscapes and provide actionable insights for improving algorithm parameterization. The framework’s modular design enables adaptation to other critical validation scenarios requiring spatial representativeness quantification.