Plant breeding programs demand efficient and accurate methods for crop phenotyping, especially for economically important crops like soybeans. High-resolution satellite and drone imagery are alternatives to ground data collection to monitor crops in a time, cost, and labor efficient way. Drone imagery offers localized and high-resolution data but has limitations in coverage and operator skills. In contrast, high-resolution satellite imagery provides broad-scale views of research sites without the need of a human data collector or pilot.

    Our study investigates the potential of high-resolution satellite imagery (50 cm GSD) as an alternative to drone imagery for assessing soybean physiological maturity and monitoring the crop condition in a small plot breeding program. We compare the efficacy of satellite against UAV by generating various multi-spectral vegetation indices (VI) to predict maturity and assessing crop canopy characteristics (green cover and NGE) . Eleven satellite derived VI were compared to UAV derived values, with NDVI (R²= 0.84), EVI2 (R²= 0.83), TVI (R²= 0.83) and IPVI (R²= 0.83) displaying the highest correlation between the two methods. Satellite-derived NDVI was also efficient in distinguishing the maturity of varieties among and across maturity groups when compared against UAV imagery. Within location heritability was high with values ranging between H²= 0.77 and H²= 0.93.

    With advances in spatial resolution, satellites can now provide detailed insights into crop health, productivity, and resource management. Our findings reveal the promise of high-resolution satellite imagery as a valuable tool in small plot phenotyping with the potential to further scale breeding programs.