Scientists have developed a new, scalable method for estimating crop productivity in real time.
University of Illinois scientists, with help from members of the Illinois Corn Growers Association, have developed a new, scalable method for estimating crop productivity in real time.
The research combines field measurements, a unique in-field camera network, and high-resolution, high-frequency satellite data, providing highly accurate productivity estimates for crops across Illinois and beyond.
Hyungsuk Kimm, a doctoral student in the Department of Natural Resources and Environmental Sciences (NRES) at University of Illinois is the lead author on the study. Kimm and his colleagues used surface reflectance data, which measures light bouncing off the Earth, from two kinds of satellites to estimate LAI in agricultural fields.
Both satellite datasets represent major improvements over older satellite technologies; they can “see” the Earth at a fine scale (3-meter or 30-meter resolution) and both return to the same spot above the planet on a daily basis. Since the satellites don’t capture LAI directly, the research team developed two mathematical algorithms to convert surface reflectance into LAI.
While developing the algorithms to estimate LAI, Kimm worked with Illinois farmers to set up cameras in 36 corn fields across the state, providing continuous ground-level monitoring. The images from the cameras provided detailed ground information to refine the satellite-derived estimates of LAI.
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The true test of the satellite estimates came from LAI data Kimm measured directly in the corn fields. Twice weekly during the 2017 growing season, he visited the fields with a specialised instrument and measured corn leaf area by hand.
In the end, the satellite LAI estimates from the two algorithms strongly agreed with Kimm’s “ground-truth” data from the fields. This result means the algorithms delivered highly accurate, reliable LAI information from space, and can be used to estimate LAI in fields anywhere in the world in real time.
“We are the first to develop scalable, high-temporal, high-resolution LAI data for farmers to use. These methods have been fully validated using an unprecedented camera network for farmland,” says Kaiyu Guan, assistant professor in the Department of NRES and Blue Waters professor at the National Center for Supercomputing Applications. He is also principal investigator on the study.
Having real-time LAI data could be instrumental for responsive management. For example, the satellite method could detect underperforming fields or segments of fields that could be corrected with targeted management practices such as nutrient management, pesticide application, or other strategies. Guan plans to make real-time data available to farmers in the near future.