Detecting crop nitrogen with hyperspectral sensors on planes

According to researcher and lead athor on the study Sheng Wang, the airplane hyperspectral sensing technique is not only very fast, it also provides much higher spectral and spatial resolution than similar studies using satellite imagery.

Wang said their 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.

An airplane was fitted with a top-of-the-line hyperspectral sensor capable of detecting wavelengths in the visible and near infrared spectrum (400-2400 nanometers). It flew over an experimental field in Illinois three times during the 2019 growing season. The researchers also took in-field leaf and canopy measurements as ground-truth data for comparison with sensor data.

Up to 85% accuracy

The flights detected leaf and canopy nitrogen characteristics, including several related to photosynthetic capacity and grain yield, with up to 85% accuracy, which is close to ground-truth quality, according to Kaiyu Guan, co-author on the study. “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,” Guan said.

Hyperspectral sensors detect differences of just 3 to 5 nanometers across their entire range, a sensitivity unmatched 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,” Guan says.

The research team also worked out the best mathematical algorithm to detect nitrogen reflectance data from the hyperspectral sensor. They expect it will be put to use as newer technologies come on board.

Bring technology to satellites

“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,” Wang says.

Guan says bringing this technology to satellites is the end goal, enabling a view of every field’s nitrogen status early in the growing season. The advancement will allow farmers to make more informed decisions about nitrogen side-dressing.