|WU, GENGHONG - University Of Illinois|
|GUAN, KAIYU - University Of Illinois|
|JIANG, CHONGYA - University Of Illinois|
|PENG, BIN - University Of Illinois|
|KIMM, HYUNGSUK - University Of Illinois|
|CHEN, MIN - Pacific Northwest National Laboratory|
|YANG, XI - University Of Virginia|
|WANG, SHENG - University Of Illinois|
|SUYKER, ANDREW - University Of Nebraska|
|MOORE, CAITLIN - University Of Illinois|
|ZENG, YELU - Stanford University|
|BERRY, JOSEPH - Stanford University|
|CENTRERO-MATEO, M - University Of Valencia|
Submitted to: Environmental Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/27/2019
Publication Date: 2/18/2020
Citation: Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang, S., Suyker, A.E., Bernacchi, C.J., Moore, C., Zeng, Y., Berry, J.A., Centrero-Mateo, M.P. 2020. Radiance-based NIRv as a proxy for GPP of corn and soybean. Environmental Research Letters. 15:034009. https://doi.org/10.1088/1748-9326/ab65cc.
Interpretive Summary: Measuring the amount of growth of plants at a field scale is very challenging. This causes error and uncertainty in the global carbon cycle estimates, as well as in the ability to predict crop yields. Here a combination of imaging approaches are used to estimate corn and soybean growth on a daily basis at three locations representing different management and climate conditions, reflecting different growth rates. The conclusions of this method indicate that very small changes in growth of crops can be determined using specialize imaging sensors, providing resolution needed to infer how climate and management impact plants. The implication of this research suggests that low-cost sensors can be used to scale measurements of crop growth from small (plot) to large (regional) scales.
Technical Abstract: 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 (NDVI), 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 high37 resolution and/or long-term satellite remote sensing data.