摘要Aboveground biomass(AGB)is a critical indicator for assessing crop growth status and productivity,yet accu-rately linking fine-scale ground measurements with coarse-resolution satellite imagery remains challenging.Here,we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observa-tions with an optimized systematic sampling-Global Moran's I(SS-GMI)procedure and a simple allometric growth model.Multi-variety sugar beet cultivated across heterogeneous habitats was used as a case study.Re-sults indicate that a power-law model effectively captures the allometric relationships between AGB,plant height,and the Dreg vegetation index in sugar beet,achieving high accuracy and strong transferability.Incor-porating phenological information from Biologische Bundesanstalt,Bundessortenamt und CHemische Industrie(BBCH)codes and a thermal index further enhanced model robustness across independent habitat trials,yielding coefficients of determination(R2)of 0.80 and 0.83.The SS-GMI sampling procedure integrates systematic sampling with Global Moran's I to reduce spatial autocorrelation while ensuring uniform spatial coverage,thereby enabling the acquisition of representative and spatially independent samples from UAV-derived AGB maps.These samples were used to develop satellite-based AGB estimation models for PlanetScope and Sentinel-2A imagery,achieving R2 values of 0.83 and 0.73,respectively.This study provides a practical and scalable framework for field-to-satellite AGB upscaling,offering new insights for the scale conversion of multi-source data in agricultural remote sensing.