Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Journal of Experimental Botany

Volumn 69, Issue 3, 2018, Pages 483-496

  Silva Perez, Viridiana ^a,b   Molero, Gemma ^d   Serbin, Shawn P ^c   Condon, Anthony G ^a,b   Reynolds, Matthew P ^d   Furbank, Robert T ^a,b   Evans, John R ^b  

^a CSIRO   (Australia)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c BROOKHAVEN NATIONAL LABORATORY   (United States)

^d INTERNATIONAL MAIZE AND WHEAT IMPROVEMENT CENTER CIMMYT   (Mexico)

Author keywords

Electron transport rate; hyperspectral reflectance; leaf dry mass per area; leaf nitrogen; partial least squares; photosynthesis; Rubisco; Triticum aestivum; velocity of carboxylation

Indexed keywords

CARBON DIOXIDE;

DEVICES; INFRARED SPECTROPHOTOMETRY; PHOTOSYNTHESIS; PHYSIOLOGY; PLANT LEAF; PROCEDURES; WHEAT;

CARBON DIOXIDE; PHOTOSYNTHESIS; PLANT LEAVES; SPECTROPHOTOMETRY, INFRARED; TRITICUM;

EID: 85041220026     PISSN: 00220957     EISSN: 14602431     Source Type: Journal    
DOI: 10.1093/jxb/erx421     Document Type: Article

References (55)

1. Ainsworth EA, Serbin SP, Skoneczka JA, Townsend PA. 2014. Using leaf optical properties to detect ozone effects on foliar biochemistry. Photosynthesis Research 119, 65-76.
2. Amani I, Fischer RA, Reynolds MP. 1996. Canopy temperature depression association with yield of irrigated spring wheat cultivars in a hot climate. Journal of Agronomy and Crop Science 176, 119-129.
3. Babar MA, Reynolds MP, van Ginkel M, Klatt AR, Raun WR, Stone ML. 2006. Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Science 46, 1046-1057.
4. Benedict HM, Swidler R. 1961. Nondestructive method for estimating chlorophyll content of leaves. Science 133, 2015-2016.
5. Bernacchi CJ, Pimentel C, Long SP. 2003. In vivo temperature response functions of parameters required to model RuBP-limited photosynthesis. Plant, Cell & Environment 26, 1419-1430.
6. Cabrera-Bosquet L, Molero G, Stellacci AM, Bort J, Nogues S, Araus JL. 2011. NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Research Communications 39, 147-159.
7. De Kauwe MG, Lin YS, Wright IJ, et al. 2016. A test of the onepoint method for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytologist 210, 1130-1144.
8. Dillen SY, Op de Beeck M, Hufkens K, Buonanduci M, Phillips NG. 2012. Seasonal patterns of foliar reflectance in relation to photosynthetic capacity and color index in two co-occurring tree species, Quercus rubra and Betula papyrifera. Agricultural and Forest Meteorology 160, 60-68.
9. Doughty CE, Asner GP, Martin RE. 2011. Predicting tropical plant physiology from leaf and canopy spectroscopy. Oecologia 165, 289-299.
10. Ecarnot M, Compan F, Roumet P. 2013. Assessing leaf nitrogen content and leaf mass per unit area of wheat in the field throughout plant cycle with a portable spectrometer. Field Crops Research 140, 44-50.
11. Farquhar GD, von Caemmerer S, Berry JA. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78-90.
12. Fischer RA, Rees D, Sayre KD, Lu ZM, Condon AG, Saavedra AL. 1998. Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Science 38, 1467-1475.
13. Fox J, Weisberg S. 2011. An R companion to applied regression. Los Angeles, London: SAGE.
14. Galicia L, Nurit E, Rosales A, Palacios-Rojas N. 2008. Maize nutrition quality and plant tissue analysis laboratory. Laboratory protocols 2008. Mexico City: CIMMYT.
15. Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Peñuelas J, Valentini R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three californian vegetation types. Ecological Applications 5, 28-41.
16. Gamon JA, Peñuelas J, Field CB. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41, 35-44.
17. Garriga M, Romero-Bravo S, Estrada F, Escobar A, Matus IA, Del Pozo A, Astudillo CA, Lobos GA. 2017. Assessing wheat traits by spectral reflectance: Do we really need to focus on predicted trait-values or directly identify the elite genotypes group? Frontiers in Plant Science 8, 280.
18. Geladi P, Kowalski BR. 1986. Partial least-squares regression-A tutorial. Analytica Chimica Acta 185, 1-17.
19. Gillon D, Houssard C, Joffre R. 1999. Using near-infrared reflectance spectroscopy to predict carbon, nitrogen and phosphorus content in heterogeneous plant material. Oecologia 118, 173-182.
20. González-Navarro OE, Griffiths S, Molero G, Reynolds MP, Slafer GA. 2015. Dynamics of floret development determining differences in spike fertility in an elite population of wheat. Field Crops Research 172, 21-31.
21. Goward SN, Tucker CJ, Dye DG. 1985. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio 64, 3-14.
22. Hansen PM, Schjoerring JK. 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86, 542-553.
23. Heckmann D, Schlöter U, Weber APM. 2017. Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra. Molecular Plant 10, 878-890.
24. Inada K. 1963. Studies on a method for determining the deepness of green and color chlorophyll content of intact crop leaves and its practical applications. Proceedings of the Crop Science Society of Japan 32, 157-162.
25. Jones HG. 2007. Monitoring plant and soil water status: established and novel methods revisited and their relevance to studies of drought tolerance. Journal of Experimental Botany 58, 119-130.
26. Jones HG, Serraj R, Loveys BR, Xiong L, Wheaton A, Price AH. 2009. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Functional Plant Biology 36, 978-989.
27. June T, Evans JR, Farquhar GD. 2004. A simple new equation for the reversible temperature dependence of photosynthetic electron transport: A study on soybean leaf. Functional Plant Biology 31, 275-283.
28. Lopes MS, Reynolds MP. 2012. Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. Journal of Experimental Botany 63, 3789-3798.
29. Mevik BH, Wehrens R. 2007. The pls package: Principal component and partial least squares regression in R. Journal of Statistical Software 18, doi: 10.18637/jss.v018.i02.
30. Mullan D, Mullan D. 2012. Chlorophyll content. In: Pask A, Pietragalla J, Mullan D, Reynolds MP, eds. Physiological breeding II: A field guide to wheat phenotyping. Mexico, DF: CIMMYT, 41-43.
31. Munns R, James RA, Sirault XR, Furbank RT, Jones HG. 2010. New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. Journal of Experimental Botany 61, 3499-3507.
32. Nguyen HT, Lee B-W. 2006. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy 24, 349-356.
33. Parry MA, Reynolds M, Salvucci ME, Raines C, Andralojc PJ, Zhu XG, Price GD, Condon AG, Furbank RT. 2011. Raising yield potential of wheat. II. Increasing photosynthetic capacity and efficiency. Journal of Experimental Botany 62, 453-467.
34. Peñuelas J, Filella I. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3, 151-156.
35. Peñuelas J, Garbulsky MF, Filella I. 2011. Photochemical reflectance index (PRI) and remote sensing of plant CO2 uptake. New Phytologist 191, 596-599.
36. Peñuelas J, Pinol J, Ogaya R, Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing 18, 2869-2875.
37. Pimstein A, Karnieli A, Bonfil DJ. 2007. Wheat and maize monitoring based on ground spectral measurements and multivariate data analysis. Journal of Applied Remote Sensing 1, 013530.
38. Pinto RS, Lopes MS, Collins NC, Reynolds MP. 2016. Modelling and genetic dissection of staygreen under heat stress. Theoretical and Applied Genetics 129, 2055-2074.
39. Rebetzke GJ, Condon AG, Richards RA, Read JJ. 2001. Phenotypic variation and sampling for leaf conductance in wheat (Triticum aestivum L.) breeding populations. Euphytica/Netherlands Journal of Plant Breeding 121, 335-341.
40. Rebetzke GJ, Rattey AR, Farquhar GD, Richards RA, Condon AG. 2013. Genomic regions for canopy temperature and their genetic association with stomatal conductance and grain yield in wheat. Functional Plant Biology 40, 14-33.
41. Reynolds M, Foulkes J, Furbank R, Griffiths S, King J, Murchie E, Parry M, Slafer G. 2012. Achieving yield gains in wheat. Plant, Cell & Environment 35, 1799-1823.
42. Serbin SP, Dillaway DN, Kruger EL, Townsend PA. 2012. Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. Journal of Experimental Botany 63, 489-502.
43. Serbin SP, Singh A, Desai AR, Dubois SG, Jablonski AD, Kingdon CC, Kruger EL, Townsend PA. 2015. Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sensing of Environment 167, 78-87.
44. Silva-Pérez V, Furbank RT, Condon AG, Evans JR. 2017. Biochemical model of C3 photosynthesis applied to wheat at different temperatures. Plant, Cell & Environment 40, 1552-1564.
45. Singh A, Serbin SP, McNeil BE, Kingdon CC, Townsend PA. 2015. Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecological Applications 25, 2180-2197.
46. Sirault XRR, James RA, Furbank RT. 2009. A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Functional Plant Biology 36, 970-977.
47. Stinziano JR, Morgan PB, Lynch DJ, Saathoff AJ, McDermitt DK, Hanson DT. 2017. The rapid A-Ci response: photosynthesis in the phenomic era. Plant, Cell & Environment 40, 1256-1262.
48. Tattaris M, Reynolds MP, Chapman SC. 2016. A direct comparison of remote sensing approaches for high-Throughput phenotyping in plant breeding. Frontiers in Plant Science 7, 1131.
49. Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 150, 127-150.
50. UN Department of Economic and Social Affairs. 2015. World population prospects. The 2015 revision. Key findings and advance tables. New York: United Nations Department of Economic and Social Affairs, Population Division.
51. von Caemmerer S. 2000. Biochemical models of leaf photosynthesis. Collingwood: CSIRO Publishing.
52. Yang X, Tang J, Mustard JF, Wu J, Zhao K, Serbin S, Lee J-E. 2016. Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests. Remote Sensing of Environment 179, 1-12.
53. Yao X, Huang Y, Shang G, Zhou C, Cheng T, Tian Y, Cao W, Zhu Y. 2015. Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sensing 7, 14939-14966.
54. Yendrek CR, Tomaz T, Montes CM, Cao Y, Morse AM, Brown PJ, McIntyre LM, Leakey AD, Ainsworth EA. 2017. High-Throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiology 173, 614-626.
55. Zadoks JC, Chang TT, Konzak CF. 1974. Decimal code for growth stages of cereals. Weed Research 14, 415-421.