Corn and soybean fields look similar from space -- at least they used to -- but scientists have proven a new technique for distinguishing the two crops using satellite data and the processing power of supercomputers.
"If we want to predict corn or soybean production for Illinois or the entire United States, we have to know where they are being grown," said Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, Blue Waters professor at the National Center for Supercomputing Applications and the new study's principal investigator.
The advancement, published in Remote Sensing of Environment, is a breakthrough because national corn and soybean acreages previously were only made available to the public four to six months after harvest by the USDA. The lag meant policy decisions were based on stale data. But the new technique can distinguish the two major crops with 95 percent accuracy by the end of July for each field -- just two or three months after planting and well before harvest.
The researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics and commodity markets.
For Guan, the work's scientific value is as important as its practical value.
It turns out corn and soybean have predictably different leaf water status by July most years. The team used short-wave infrared data and other spectral data from three Landsat satellites over a 15-year period and consistently picked up this leaf water status signal.
"The SWIR band is more sensitive to water content inside the leaf. That signal can't be captured by traditional RGB (visible) light or near-infrared bands, so the SWIR is extremely useful to differentiate corn and soybean," Guan said.
Corn hybrid variability
For corn breeders tasked with feeding a growing population and minimizing the environmental footprint, that means improving nitrogen-use efficiency and crowding tolerance all while maximizing yield.
A new study from the University of Illinois said the first step is understanding the genetic yield potential of current hybrids.
"Growers and breeding programs need to understand which hybrids have stable yields across environments or are able to produce greater yields with more fertilizer and higher plant populations," said Fred Below, professor of crop physiology in the Department of Crop Sciences and study co-author.
A hybrid with high yield stability is less responsive to the environment -- it will perform consistently in suboptimal and optimal conditions. Alternatively, a hybrid with high adaptability will yield like gangbusters when planted in optimal conditions but may let farmers down in a bad year.
The problem is that current commercial breeding programs develop their elite hybrids under optimal conditions, so Below and his team evaluated 101 commercially available elite hybrids at two planting densities and three nitrogen fertilizer rates across multiple years and locations.
The researchers found that the amount of applied nitrogen fertilizer had a much greater effect on yield than planting density, but they emphasize that the consistency of the yield response was more important.
Hybrids that combined above-average yield under unfertilized and low-nitrogen conditions exhibited more consistent yields regardless of the environment -- or best used in nitrogen-loss prone areas or when yield stability is more desired. In contrast, other hybrids yielded more under high-nitrogen than low-nitrogen conditions, but their yields were more variable due to a greater sensitivity to environmental conditions.