Nitrogen (N) fertilizer management is one of the main concerns for precision agriculture under corn production, which aims to not only maximize the profits, but also ensure environmental sustainability. Effective N fertilizer management can either avoid N stress or provide timely and accurate detection of in-season N stress for remedies. Traditional N trial experiments to evaluate different N management practices have to wait until harvest, and do not allow tracking of when and how N stress develops. Meanwhile, rapidly developed remote sensing technology offers new opportunities for in-season evaluation of N status and detection of N stress for crops, including both the unmanned aircraft vehicle (UAV)-based and satellite-based multispectral sensing. In this study, we collected weekly multispectral images of UAV and Planet Lab's CubeSat, as well as various other ground measurements for an experimental cornfield that included 28 N management treatments in Central Illinois, 2017. We found that both the UAV-and CubeSat-based multispectral sensors were able to detect N stress at vegetative stages before tasseling, and could detect changes in the level of N stress through derived chlorophyll index green (CIg) for different N management practices. The CubeSat-based CIg showed high consistency with the UAV-based CIg (correlation above 0.9), which indicated the potential of CubeSat-based CIg to be applied for N stress detection at a larger spatial scale. This study demonstrates that the UAV-and CubeSat-based multispectral sensing has the promising potential to monitor N stress of corn throughout the growing season, which may assist decision making of N management.

Original languageEnglish (US)
Article number8950295
Pages (from-to)5153-5166
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number12
StatePublished - Dec 2019


  • Corn nitrogen stress
  • CubeSat
  • Planet Lab
  • in-season detection
  • unmanned aerial vehicles (UAV)

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science


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