An offline, position-indexed channel-direction database can support location-aware unmanned aerial vehicle (UAV) beamforming, but sparse measurements leave intervals with different directional consistency. We propose Sampling-Adaptive Gauge-Projective Channel-Direction Reconstruction (SAGP-CDR). It treats normalized channel vectors that differ only by a common phase as the same array direction. Each anchor interval selects one of three reconstruction methods: anchor-calibrated line of sight (LOS), an aligned along-track chord, or a three-dimensional spatial affine fit. Four anchor-observable descriptors describe physical gap, endpoint agreement, spatial-neighbor agreement and directional turn. The LOS method additionally uses the station position and an assumed array geometry. Retrospective nested leave-one-flight-out evaluation uses five measured massive-MIMO UAV flights from one campaign, with 64 antennas and 100 subcarriers. The primary loss takes a median over query positions at each subcarrier, then a median across subcarriers. Across 450 matched masks, SAGP-CDR lowers mean primary direction loss by 30.25% relative to anchor-tuned aligned Gaussian interpolation. Reductions relative to the strongest fixed comparator are 14.24%, 18.84% and 3.27% at 2%, 5% and 10% retention. At 2%, loss is 0.1705 versus 0.2682 for three-dimensional nearest-anchor reuse. Relative to a global rate control calibrated on the primary metric with identical candidates, the aggregate reduction is 14.87%. These results support observable local selection for sparse channel-direction reconstruction within the evaluated campaign.