Plot-level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials.

Abstract

Abstract Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening thousands of varieties, and policymakers in decision-making contributing to the improvement of agriculture and food production systems. Effective approaches to track plant growth and predict yield require large datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional scales. Despite their lower spatial resolution, satellite platforms exhibit logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments with ground truth measurements are not yet publicly available. Here we generated, described, and evaluated over 20,000 plot-level images of over 80 hybrid maize varieties grown across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth yield measurement. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing drone images for both within and cross-environment yield prediction. Large, multi-environment, genetically diverse datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important addition to the tool of farmers, plant geneticists, breeders, and policymakers.