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

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 high-resolution and/or long-term satellite remote sensing data.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2];  [3];  [1];  [4]; ORCiD logo [5]; ORCiD logo [1];  [6];  [6]; ORCiD logo [7]
  1. Univ. of Illinois at Urbana-Champaign, IL (United States)
  2. Pacific Northwest National Lab. (PNNL), College Park, MA (United States). Joint Global Change Research Inst.
  3. Univ. of Virginia, Charlottesville, VA (United States)
  4. Univ. of Nebraska, Lincoln, NE (United States)
  5. Univ. of Illinois at Urbana-Champaign, IL (United States); US Dept. of Agriculture (USDA), Urbana, IL (United States). Agricultural Research Service
  6. Carnegie Inst. for Science, Stanford, CA (United States)
  7. Univ. of Valencia (Spain)
Publication Date:
Research Org.:
Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1599080
Grant/Contract Number:  
SC0018420
Resource Type:
Accepted Manuscript
Journal Name:
Environmental Research Letters
Additional Journal Information:
Journal Volume: 15; Journal Issue: 3; Journal ID: ISSN 1748-9326
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
photosynthesis, gross primary production, NIRv, near-infrared radiance

Citation Formats

Wu, Genghong, Guan, Kaiyu, Jiang, Chongya, Peng, Bin, Kimm, Hyungsuk, Chen, Min, Yang, Xi, Wang, Sheng, Suyker, Andrew E., Bernacchi, Carl J., Moore, Caitlin E., Zeng, Yelu, Berry, Joseph A., and Cendrero-Mateo, M. Pilar. Radiance-based NIRv as a proxy for GPP of corn and soybean. United States: N. p., 2020. Web. doi:10.1088/1748-9326/ab65cc.
Wu, Genghong, Guan, Kaiyu, Jiang, Chongya, Peng, Bin, Kimm, Hyungsuk, Chen, Min, Yang, Xi, Wang, Sheng, Suyker, Andrew E., Bernacchi, Carl J., Moore, Caitlin E., Zeng, Yelu, Berry, Joseph A., & Cendrero-Mateo, M. Pilar. Radiance-based NIRv as a proxy for GPP of corn and soybean. United States. doi:https://doi.org/10.1088/1748-9326/ab65cc
Wu, Genghong, Guan, Kaiyu, Jiang, Chongya, Peng, Bin, Kimm, Hyungsuk, Chen, Min, Yang, Xi, Wang, Sheng, Suyker, Andrew E., Bernacchi, Carl J., Moore, Caitlin E., Zeng, Yelu, Berry, Joseph A., and Cendrero-Mateo, M. Pilar. Tue . "Radiance-based NIRv as a proxy for GPP of corn and soybean". United States. doi:https://doi.org/10.1088/1748-9326/ab65cc. https://www.osti.gov/servlets/purl/1599080.
@article{osti_1599080,
title = {Radiance-based NIRv as a proxy for GPP of corn and soybean},
author = {Wu, Genghong and Guan, Kaiyu and Jiang, Chongya and Peng, Bin and Kimm, Hyungsuk and Chen, Min and Yang, Xi and Wang, Sheng and Suyker, Andrew E. and Bernacchi, Carl J. and Moore, Caitlin E. and Zeng, Yelu and Berry, Joseph A. and Cendrero-Mateo, M. Pilar},
abstractNote = {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 high-resolution and/or long-term satellite remote sensing data.},
doi = {10.1088/1748-9326/ab65cc},
journal = {Environmental Research Letters},
number = 3,
volume = 15,
place = {United States},
year = {2020},
month = {2}
}

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