While new technologies can capture high-resolution normalized difference vegetation index (NDVI), a surrogate for biomass and leaf greenness, it is a challenge to efficiently apply this technology in a large breeding program. Here we validate a high-throughput phenotyping platform to dynamically monitor NDVI during the growing season for the contrasting wheat cultivars and managements. The images were rapidly captured (approximately 1 ha in 10 min) by an unmanned aerial vehicle (UAV) carrying a multi-spectral camera (RedEdge) at low altitude (30–50 m, 2–5 cm2 pixel size). NDVIs for individual plots were extracted from the reflectance at Red and Near Infrared wavelengths represented in a reconstructed and segmented ortho-mosaic. NDVI measured by UAV and RedEdge camera were strongly correlated with those measured by hand held GreenSeeker (R2 = 0.85) but were offset with UAV readings about 0.2 units higher and more compressed. The high-throughput phenotyping platform captured the variation of NDVI among cultivars and treatments (i.e. irrigation, nitrogen and sowing). During the growing season, the NDVI approached saturation around flowering time (∼0.92), then gradually decreased until maturity (∼0.35). Strong correlations were found between image NDVI around flowering time and final yield (R2 = 0.82). Given that the image NDVI includes signals from background (soil and senescenced leaves), ground cover from a high resolution hand-held camera was used to adjust the NDVI from UAV. This slightly increased the correlation between adjusted NDVI and yield (R2 = 0.87). The high-throughput phenotyping platform in this study can be used in agronomy, physiology and breeding to explore the complex interaction of genotype, environment and management. Data fusion from ground and aerial sampling improved the accuracy of low resolution data to integrate observations across multiple scales.