Radiance-based NIRv as a proxy for GPP of corn and soybean

Genghong Wu, Kaiyu Guan, Chongya Jiang, Bin Peng, Hyungsuk Kimm, Min Chen, Xi Yang, Sheng Wang, Andrew E. Suyker, Carl J. Bernacchi, Caitlin E. Moore, Yelu Zeng, Joseph A. Berry, M. Pilar Cendrero-Mateo

Research output: Contribution to journalArticlepeer-review


Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIRv,Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIRv,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIRv,Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF760). The strong linear relationship between NIRv,Rad and absorbed photosynthetically active radiation by green leaves (APARgreen), and that between APARgreen and GPP, explain the good NIRv,Rad-GPP relationship. The NIRv,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIRv,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.

Original languageEnglish (US)
Article number034009
JournalEnvironmental Research Letters
Issue number3
StatePublished - 2020


  • NIRv
  • gross primary production
  • near-infrared radiance of vegetation
  • photosynthesis

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Environmental Science
  • Public Health, Environmental and Occupational Health


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