Satellite imagery for high-throughput phenotyping in breeding plots

Area of Science:

• Agricultural Science
• Remote Sensing
• Plant Breeding

Background:

• Crop breeding advancements are hindered by costly and complex phenotyping methods.
• Satellite imagery offers a scalable solution for remote crop monitoring, but low resolution has been a limitation.
• New high-resolution satellites present an opportunity to overcome previous spatial resolution constraints.

Purpose of the Study:

• To evaluate the utility of time-series SkySat imagery for phenotyping wheat and maize breeding plots.
• To assess the reliability of NDVI estimation from high-resolution satellite data.
• To determine the capacity of this approach for detecting seasonal changes and genotypic variations in breeding trials.

Main Methods:

• Utilized time-series multispectral images from the SkySat constellation (0.5m resolution).
• Estimated Normalized Difference Vegetation Index (NDVI) for wheat and maize plots of varying sizes and spacing.
• Evaluated NDVI reliability, seasonal changes, and genotypic difference detection.

Main Results:

• SkySat imagery reliably estimated NDVI in wheat and maize breeding plots.
• The approach successfully detected seasonal vegetation changes.
• Genotypic differences within breeding plots were discernible using this method.

Conclusions:

• High-resolution satellite phenotyping using SkySat is a viable tool for crop breeding.
• This technology can enhance high-throughput phenotyping, reduce costs, and standardize protocols.
• Future applications include broader implementation in breeding programs for faster genetic gains and improved crop resilience.