Crop performance, aerial, and satellite data from multistate maize yield trials

Accurate genotype-specific early yield estimates at fields and plots offer
potential benefits to farmers in optimizing their agronomic practices,
breeders in screening hundreds and thousands of varieties, and
policymakers in decisions contributing to the overall improvement of
agriculture and food production systems. Effective, generalizable
approaches to track plant growth and predict yield at the individual plot
level require large matched 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 and field scales. Despite their
lower spatial resolution, satellite platforms exhibit multiple 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 integrated
with the ground truth measurements are not yet publicly available. Here we
generated, described, and evaluated a set of more than 20,000 plot-level
images of over 80 hybrid maize (Zea mays) varieties grown in six locations
across the US corn belt under various management practices collected from
(near simultaneous) satellite and drone flights integrated with ground
truth measurements of crop yield. Of the six baseline models examined,
models employing data collected from satellite images often matched or
exceeded the performance of models employing data collected from drones
for both within-environment and cross-environment yield prediction. Large,
multimodal, multi-environment, genetically diverse training datasets such
as those generated in this study, along with more complex models could
help unlock the power of satellite imagery as an important new addition to
the tool of farmers, plant geneticists, crop breeders, and policymakers.