Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

Wang, Sheng; Guan, Kaiyu; Wang, Zhihui; Ainsworth, Elizabeth A.; Zheng, Ting; Townsend, Philip A.; Li, Kaiyuan; Moller, Christopher; Wu, Genghong; Jiang, Chongya

doi:10.1093/jxb/eraa432

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

Journal Article · Wed Sep 16 00:00:00 EDT 2020 · Journal of Experimental Botany

DOI:https://doi.org/10.1093/jxb/eraa432· OSTI ID:1696777

^[1]; Guan, Kaiyu ^[2]; Wang, Zhihui ^[3]; Ainsworth, Elizabeth A. ^[4]; Zheng, Ting ^[3]; Townsend, Philip A. ^[3]; Li, Kaiyuan ^[2]; Moller, Christopher ^[2]; Wu, Genghong ^[2]; Jiang, Chongya ^[2]

Univ. of Illinois at Urbana-Champaign, IL (United States); CABBI
Univ. of Illinois at Urbana-Champaign, IL (United States)
Univ. of Wisconsin, Madison, WI (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States); USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL (United States)

The photosynthetic capacity or the CO₂-saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C₄ crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V_max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V_max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V_max prediction.

View Accepted Manuscript (DOE)

Research Organization:: Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018420

OSTI ID:: 1696777

Journal Information:: Journal of Experimental Botany, Journal Name: Journal of Experimental Botany Journal Issue: 2 Vol. 72; ISSN 0022-0957

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

References (68)

The relationship of leaf photosynthetic traits - V _cmax and J _max - to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta-analysis and modeling study Walker, Anthony P.; Beckerman, Andrew P.; Gu, Lianhong Ecology and Evolution, Vol. 4, Issue 16 https://doi.org/10.1002/ece3.1173	journal	July 2014
The Correlation between Plant Growth and Intercepted Radiation: An Interpretation in Terms of Optimal Plant Nitrogen Content Dewar, R. Annals of Botany, Vol. 78, Issue 1 https://doi.org/10.1006/anbo.1996.0104	journal	July 1996
Solubilization of ribulose-1,5-bisphosphate carboxylase from the membrane fraction of pea leaves Makino, Amane; Osmond, Barry Photosynthesis Research, Vol. 29, Issue 2 https://doi.org/10.1007/BF00035378	journal	August 1991
A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species Farquhar, G. D.; von Caemmerer, S.; Berry, J. A. Planta, Vol. 149, Issue 1 https://doi.org/10.1007/BF00386231	journal	June 1980
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods Verrelst, Jochem; Malenovský, Zbyněk; Van der Tol, Christiaan Surveys in Geophysics, Vol. 40, Issue 3 https://doi.org/10.1007/s10712-018-9478-y	journal	June 2018
Using leaf optical properties to detect ozone effects on foliar biochemistry Ainsworth, Elizabeth A.; Serbin, Shawn P.; Skoneczka, Jeffrey A. Photosynthesis Research, Vol. 119, Issue 1-2 https://doi.org/10.1007/s11120-013-9837-y	journal	May 2013
Partial least-squares regression: a tutorial Geladi, Paul; Kowalski, Bruce R. Analytica Chimica Acta, Vol. 185 https://doi.org/10.1016/0003-2670(86)80028-9	journal	January 1986
Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model Verhoef, W. Remote Sensing of Environment, Vol. 16, Issue 2 https://doi.org/10.1016/0034-4257(84)90057-9	journal	October 1984
The semivariogram in remote sensing: An introduction Curran, Paul J. Remote Sensing of Environment, Vol. 24, Issue 3 https://doi.org/10.1016/0034-4257(88)90021-1	journal	April 1988
PROSPECT: A model of leaf optical properties spectra Jacquemoud, S.; Baret, F. Remote Sensing of Environment, Vol. 34, Issue 2 https://doi.org/10.1016/0034-4257(90)90100-Z	journal	November 1990
Leaf protein contents and nitrogen-to-protein conversion factors for 90 plant species Yeoh, Hock-Hin; Wee, Yeow-Chin Food Chemistry, Vol. 49, Issue 3 https://doi.org/10.1016/0308-8146(94)90167-8	journal	January 1994
PLS-regression: a basic tool of chemometrics Wold, Svante; Sjöström, Michael; Eriksson, Lennart Chemometrics and Intelligent Laboratory Systems, Vol. 58, Issue 2 https://doi.org/10.1016/S0169-7439(01)00155-1	journal	October 2001
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates Sobol′, I. M. Mathematics and Computers in Simulation, Vol. 55, Issue 1-3 https://doi.org/10.1016/S0378-4754(00)00270-6	journal	February 2001
Satellite retrievals of leaf chlorophyll and photosynthetic capacity for improved modeling of GPP Houborg, Rasmus; Cescatti, Alessandro; Migliavacca, Mirco Agricultural and Forest Meteorology, Vol. 177 https://doi.org/10.1016/j.agrformet.2013.04.006	journal	August 2013
Optimizing the photosynthetic parameter Vcmax by assimilating MODIS-fPAR and MODIS-NDVI with a process-based ecosystem model Hu, Shi; Mo, Xingguo; Lin, Zhonghui Agricultural and Forest Meteorology, Vol. 198-199 https://doi.org/10.1016/j.agrformet.2014.09.002	journal	November 2014
Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest Wang, Sheng; Ibrom, Andreas; Bauer-Gottwein, Peter Agricultural and Forest Meteorology, Vol. 248 https://doi.org/10.1016/j.agrformet.2017.10.023	journal	January 2018
Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery Wang, Zhihui; Skidmore, Andrew K.; Darvishzadeh, Roshanak Agricultural and Forest Meteorology, Vol. 253-254 https://doi.org/10.1016/j.agrformet.2018.02.010	journal	May 2018
The yield gap of global grain production: A spatial analysis Neumann, Kathleen; Verburg, Peter H.; Stehfest, Elke Agricultural Systems, Vol. 103, Issue 5 https://doi.org/10.1016/j.agsy.2010.02.004	journal	June 2010
Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments Chou, Shuren; Chen, Bin; Chen, Jing Ecological Indicators, Vol. 110 https://doi.org/10.1016/j.ecolind.2019.105867	journal	March 2020
Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes Weber, V. S.; Araus, J. L.; Cairns, J. E. Field Crops Research, Vol. 128 https://doi.org/10.1016/j.fcr.2011.12.016	journal	March 2012
Photochemical reflectance ratio for tracking light use efficiency for sunlit leaves in two forest types Zheng, Ting; Chen, Jing M. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 123 https://doi.org/10.1016/j.isprsjprs.2016.11.005	journal	January 2017
Leaf chlorophyll constraint on model simulated gross primary productivity in agricultural systems Houborg, Rasmus; F. McCabe, Matthew; Cescatti, Alessandro International Journal of Applied Earth Observation and Geoinformation, Vol. 43 https://doi.org/10.1016/j.jag.2015.03.016	journal	December 2015
Reflectance quantities in optical remote sensing—definitions and case studies Schaepman-Strub, G.; Schaepman, M. E.; Painter, T. H. Remote Sensing of Environment, Vol. 103, Issue 1 https://doi.org/10.1016/j.rse.2006.03.002	journal	July 2006
PROSPECT+SAIL models: A review of use for vegetation characterization Jacquemoud, Stéphane; Verhoef, Wout; Baret, Frédéric Remote Sensing of Environment, Vol. 113 https://doi.org/10.1016/j.rse.2008.01.026	journal	September 2009
Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy Serbin, Shawn P.; Singh, Aditya; Desai, Ankur R. Remote Sensing of Environment, Vol. 167 https://doi.org/10.1016/j.rse.2015.05.024	journal	September 2015
Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves Wang, Zhihui; Skidmore, Andrew K.; Wang, Tiejun Remote Sensing of Environment, Vol. 168 https://doi.org/10.1016/j.rse.2015.07.007	journal	October 2015
A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy Jay, Sylvain; Bendoula, Ryad; Hadoux, Xavier Remote Sensing of Environment, Vol. 177 https://doi.org/10.1016/j.rse.2016.02.029	journal	May 2016
Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems Meerdink, Susan K.; Roberts, Dar A.; King, Jennifer Y. Remote Sensing of Environment, Vol. 186 https://doi.org/10.1016/j.rse.2016.08.003	journal	December 2016
PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle Féret, J. -B.; Gitelson, A. A.; Noble, S. D. Remote Sensing of Environment, Vol. 193 https://doi.org/10.1016/j.rse.2017.03.004	journal	May 2017
Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism Dechant, Benjamin; Cuntz, Matthias; Vohland, Michael Remote Sensing of Environment, Vol. 196 https://doi.org/10.1016/j.rse.2017.05.019	journal	July 2017
The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields Guan, Kaiyu; Wu, Jin; Kimball, John S. Remote Sensing of Environment, Vol. 199 https://doi.org/10.1016/j.rse.2017.06.043	journal	September 2017
Integrating satellite optical and thermal infrared observations for improving daily ecosystem functioning estimations during a drought episode Bayat, Bagher; van der Tol, Christiaan; Verhoef, Wouter Remote Sensing of Environment, Vol. 209 https://doi.org/10.1016/j.rse.2018.02.027	journal	May 2018
Mapping foliar functional traits and their uncertainties across three years in a grassland experiment Wang, Zhihui; Townsend, Philip A.; Schweiger, Anna K. Remote Sensing of Environment, Vol. 221 https://doi.org/10.1016/j.rse.2018.11.016	journal	February 2019
High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity Meacham-Hensold, Katherine; Montes, Christopher M.; Wu, Jin Remote Sensing of Environment, Vol. 231 https://doi.org/10.1016/j.rse.2019.04.029	journal	September 2019
Radiative transfer Vcmax estimation from hyperspectral imagery and SIF retrievals to assess photosynthetic performance in rainfed and irrigated plant phenotyping trials Camino, Carlos; Gonzalez-Dugo, Victoria; Hernandez, Pilar Remote Sensing of Environment, Vol. 231 https://doi.org/10.1016/j.rse.2019.05.005	journal	September 2019
Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra Li, Dong; Tian, Long; Wan, Zefu Remote Sensing of Environment, Vol. 231 https://doi.org/10.1016/j.rse.2019.111240	journal	September 2019
Summarizing multiple aspects of model performance in a single diagram Taylor, Karl E. Journal of Geophysical Research: Atmospheres, Vol. 106, Issue D7 https://doi.org/10.1029/2000JD900719	journal	April 2001
Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents Lichtenthaler, Hartmut K.; Wellburn, Alan R. Biochemical Society Transactions, Vol. 11, Issue 5 https://doi.org/10.1042/bst0110591	journal	October 1983
Temperature dependence of two parameters in a photosynthesis model Leuning, R. Plant, Cell and Environment, Vol. 25, Issue 9 https://doi.org/10.1046/j.1365-3040.2002.00898.x	journal	September 2002
Biochemical Models of Leaf Photosynthesis von Caemmerer, S. https://doi.org/10.1071/9780643103405	book	January 2000
A theoretical analysis of nitrogen and radiation effects on radiation use efficiency in peanut Hammer, Gl; Wright, Gc Australian Journal of Agricultural Research, Vol. 45, Issue 3 https://doi.org/10.1071/AR9940575	journal	January 1994
Rubisco activase constrains the photosynthetic potential of leaves at high temperature and CO2 Crafts-Brandner, S. J.; Salvucci, M. E. Proceedings of the National Academy of Sciences, Vol. 97, Issue 24 https://doi.org/10.1073/pnas.230451497	journal	November 2000
Evidence for Compensatory Photosynthetic and Yield Response of Soybeans to Aphid Herbivory Kucharik, Christopher J.; Mork, Amelia C.; Meehan, Timothy D. Journal of Economic Entomology, Vol. 109, Issue 3 https://doi.org/10.1093/jee/tow066	journal	April 2016
Gas exchange measurements, what can they tell us about the underlying limitations to photosynthesis? Procedures and sources of error Long, S. P. Journal of Experimental Botany, Vol. 54, Issue 392 https://doi.org/10.1093/jxb/erg262	journal	September 2003
Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature Serbin, Shawn P.; Dillaway, Dylan N.; Kruger, Eric L. Journal of Experimental Botany, Vol. 63, Issue 1 https://doi.org/10.1093/jxb/err294	journal	October 2011
The nitrogen cost of photosynthesis Evans, John R.; Clarke, Victoria C. Journal of Experimental Botany, Vol. 70, Issue 1 https://doi.org/10.1093/jxb/ery366	journal	October 2018
High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance Yendrek, Craig R.; Tomaz, Tiago; Montes, Christopher M. Plant Physiology, Vol. 173, Issue 1 https://doi.org/10.1104/pp.16.01447	journal	November 2016
Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central appalachian mountains using hyperion and aviris Townsend, P. A.; Foster, J. R.; Chastain, R. A. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, Issue 6 https://doi.org/10.1109/TGRS.2003.813205	journal	June 2003
Global photosynthetic capacity is optimized to the environment Smith, Nicholas G.; Keenan, Trevor F.; Colin Prentice, I. Ecology Letters, Vol. 22, Issue 3 https://doi.org/10.1111/ele.13210	journal	January 2019
Leaf chlorophyll content as a proxy for leaf photosynthetic capacity Croft, Holly; Chen, Jing M.; Luo, Xiangzhong Global Change Biology, Vol. 23, Issue 9 https://doi.org/10.1111/gcb.13599	journal	January 2017
Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content Luo, Xiangzhong; Croft, Holly; Chen, Jing M. Global Change Biology https://doi.org/10.1111/gcb.14624	journal	April 2019
Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models Kattge, Jens; Knorr, Wolfgang; Raddatz, Thomas Global Change Biology, Vol. 15, Issue 4 https://doi.org/10.1111/j.1365-2486.2008.01744.x	journal	April 2009
Improved temperature response functions for models of Rubisco-limited photosynthesis Bernacchi, C. J.; Singsaas, E. L.; Pimentel, C. Plant, Cell and Environment, Vol. 24, Issue 2 https://doi.org/10.1111/j.1365-3040.2001.00668.x	journal	February 2001
The response of photosynthesis and stomatal conductance to rising [CO ₂ ]: mechanisms and environmental interactions : Photosynthesis and stomatal conductance responses to rising [CO Ainsworth, Elizabeth A.; Rogers, Alistair Plant, Cell & Environment, Vol. 30, Issue 3 https://doi.org/10.1111/j.1365-3040.2007.01641.x	journal	January 2007
The temperature response of C ₃ and C ₄ photosynthesis Sage, Rowan F.; Kubien, David S. Plant, Cell & Environment, Vol. 30, Issue 9 https://doi.org/10.1111/j.1365-3040.2007.01682.x	journal	September 2007
A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis De Kauwe, Martin G.; Lin, Yan-Shih; Wright, Ian J. New Phytologist, Vol. 210, Issue 3 https://doi.org/10.1111/nph.13815	journal	December 2015
Physiological and structural tradeoffs underlying the leaf economics spectrum Onoda, Yusuke; Wright, Ian J.; Evans, John R. New Phytologist, Vol. 214, Issue 4 https://doi.org/10.1111/nph.14496	journal	March 2017
Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest Albert, Loren P.; Wu, Jin; Prohaska, Neill New Phytologist, Vol. 219, Issue 3 https://doi.org/10.1111/nph.15056	journal	March 2018
Estimating photosynthetic capacity from leaf reflectance and Chl fluorescence by coupling radiative transfer to a model for photosynthesis Vilfan, Nastassia; Tol, Christiaan; Verhoef, Wouter New Phytologist, Vol. 223, Issue 1 https://doi.org/10.1111/nph.15782	journal	April 2019
Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests Wu, Jin; Rogers, Alistair; Albert, Loren P. New Phytologist, Vol. 224, Issue 2 https://doi.org/10.1111/nph.16029	journal	June 2019
Foliar functional traits from imaging spectroscopy across biomes in eastern North America Wang, Zhihui; Chlus, Adam; Geygan, Ryan New Phytologist, Vol. 228, Issue 2 https://doi.org/10.1111/nph.16711	journal	June 2020
Steady-state models of photosynthesis: Steady-state models of photosynthesis Von Caemmerer, Susanne Plant, Cell & Environment, Vol. 36, Issue 9 https://doi.org/10.1111/pce.12098	journal	April 2013
Has photosynthetic capacity increased with 80 years of soybean breeding? An examination of historical soybean cultivars: Photosynthesis in historical soybean cultivars Koester, Robert P.; Nohl, Brittany M.; Diers, Brian W. Plant, Cell & Environment, Vol. 39, Issue 5 https://doi.org/10.1111/pce.12675	journal	February 2016
Seasonal responses of photosynthetic parameters in maize and sunflower and their relationship with leaf functional traits Miner, Grace L.; Bauerle, William L. Plant, Cell & Environment, Vol. 42, Issue 5 https://doi.org/10.1111/pce.13511	journal	January 2019
Water Use Efficiency as a Constraint and Target for Improving the Resilience and Productivity of C ₃ and C ₄ Crops Leakey, Andrew D. B.; Ferguson, John N.; Pignon, Charles P. Annual Review of Plant Biology, Vol. 70, Issue 1 https://doi.org/10.1146/annurev-arplant-042817-040305	journal	April 2019
Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E. Ecological Applications, Vol. 25, Issue 8 https://doi.org/10.1890/14-2098.1	journal	December 2015
Variation in Lutein, β-carotene, and Chlorophyll Concentrations among Brassica oleracea Cultigens and Seasons Kopsell, Dean A.; Kopsell, David E.; Lefsrud, Mark G. HortScience, Vol. 39, Issue 2 https://doi.org/10.21273/HORTSCI.39.2.361	journal	April 2004
Within-Leaf Nitrogen Allocation in Adaptation to Low Nitrogen Supply in Maize during Grain-Filling Stage Mu, Xiaohuan; Chen, Qinwu; Chen, Fanjun Frontiers in Plant Science, Vol. 7 https://doi.org/10.3389/fpls.2016.00699	journal	May 2016

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Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

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