Artificial nitrogen fertilizers tremendously altered agriculture during the Green Revolution, propelling crop yields and food security to great heights.
Hyperspectral sensors affixed to small aircraft can accurately detect nitrogen status of crops. (Image Credit: University of Illinois).
Yet, in spite of advancements in crop nitrogen application efficiency, fears of underperformance push farmers to overuse fertilizer to this day. Surplus nitrogen then finds its way into waterways, including groundwater, and into the air in the form of powerful greenhouse gases.
Estimating the amount of nitrogen required by a specific crop in a specific year is not simple. The primary step is to understand the status of crop nitrogen in real-time, but it is neither practical nor scalable to quantify leaf nitrogen by hand spanning the course of a season.
In a first-of-its-kind study, scientists from the University of Illinois placed hyperspectral sensors on planes to rapidly and accurately identify the status of nitrogen and the photosynthetic capacity in corn.
Field nitrogen measurements are very time- and labor-consuming, but the airplane hyperspectral sensing technique allows us to scan the fields very fast, at a few seconds per acre. It also provides much higher spectral and spatial resolution than similar studies using satellite imagery.
Sheng Wang, Study Lead and Research Assistant Professor, Agroecosystem Sustainability Center and Department of Natural Resources and Environmental Sciences, University of Illinois
“Our approach fills a gap between field measurements and satellites and provides a cost-effective and highly accurate approach to crop nitrogen management in sustainable precision agriculture,” Wang added.
During the 2019 growing season, a plane, fixed with a topnotch sensor capable of sensing wavelengths in the visible and near-infrared range (400-2400 nm), flew over an experimental field in Illinois three times. The team also captured in-field leaf and canopy measurements as ground-truth data to compare with the sensor data.
The flights detected canopy and leaf nitrogen characteristics, including several related to grain yield and photosynthetic capacity, with up to 85% precision.
That’s close to ground-truth quality. We can even rely on the airborne hyperspectral sensors to replace ground-truth collection without sacrificing much accuracy. Meanwhile, airborne sensors allow us to cover much larger areas at low cost.
Kaiyu Guan, Study Co-Author and Founding Director of Agroecosystem Sustainability Center, University of Illinois
Guan is also an associate professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois.
Remote sensing detects energy reflected from surfaces on the ground. The chemical composition of leaves, including their chlorophyll and nitrogen content, subtly alters the amount of energy reflected. Hyperspectral sensors spot differences of just 3-5 nm across their whole range, a sensitivity unparalleled by other remote sensing technologies.
Other airborne remote sensing technologies pick up the visible spectrum and possibly near-infrared, just four spectral bands. That’s not even close to what we can do with this hyperspectral sensor. It’s really powerful.
Kaiyu Guan, Study Co-Author and Founding Director of Agroecosystem Sustainability Center, University of Illinois
Notably, the researchers came up with the best mathematical algorithm to identify nitrogen reflectance data from the hyperspectral sensor. They hope it will be applied as newer technologies come into the market.
NASA is planning a new satellite hyperspectral mission, as are other commercial satellite companies. Our study can potentially provide the algorithm for those missions because we already demonstrated its accuracy in the aircraft hyperspectral data.
Sheng Wang, Study Lead and Research Assistant Professor, Agroecosystem Sustainability Center and Department of Natural Resources and Environmental Sciences, University of Illinois
Guan explains that placing this technology on satellites is the end goal, thereby allowing a view of the nitrogen status of every field early in the growing season. The innovation will enable farmers to make better decisions about nitrogen side-dressing.
In due course, the goal is to enhance the environmental sustainability of nitrogen fertilizers in agricultural systems. Guan says accuracy is the road to achieving that.
“Essentially, you can't manage what you can't measure. That is why we put so much effort into this technology.”
This research received support from the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) SMARTFARM (MBC Lab and SYMFONI) projects.
Journal Reference:
Wang, S., et al. (2021) Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling. International Journal of Applied Earth Observation and Geoinformation. doi.org/10.1016/j.jag.2021.102617.