Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping

Remote Sensing

Volumn 7, Issue 10, 2015, Pages 13586-13605

  Gonzalez Dugo, Victoria ^a   Hernandez, Pilar ^a   Solis, Ignacio ^b   Zarco Tejada, Pablo J ^a  

^a INSTITUTO DE AGRICULTURA SOSTENIBLE   (Spain)

^b Agrovegetal SA   (Spain)

Author keywords

Chlorophyll fluorescence; Field based phenotyping; Hyperspectral; Thermal imaging; Water stress

Indexed keywords

CHLOROPHYLL; CROPS; FLUORESCENCE; INDEXING (MATERIALS WORKING); INFRARED IMAGING; PHYSIOLOGY; PLANTS (BOTANY); REMOTE SENSING; SPECTROSCOPY;

CHLOROPHYLL FLUORESCENCE; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL; HYPERSPECTRAL REMOTE SENSING; PHENOTYPING; PHOTOSYNTHETIC PIGMENTS; PHYSIOLOGICAL MEASUREMENT; WATER STRESS;

HYPERSPECTRAL IMAGING;

EID: 84945911113     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs71013586     Document Type: Article

References (58)

1. Braun, H.J.; Atlin, G.; Payne, T. Multi-location testing as a tool to identify plant response to global climate change. In Climate Change and Crop Production; Matthew P.R., Eds.; CAB International: Cambridge, MA, USA, 2010; pp 115-138.
2. Ray, D.K.; Mueller, N.D.; West, P.C.; Foley, J.A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 2013, 8, doi:10.1371/journal.pone.0066428.
3. Fischer, R.A.; Edmeades, G.O. Breeding and cereal yield progress. Crop Sci. 2010, 50, 85-98.
4. Passioura, J.B. Phenotyping for drought tolerance in grain crops: When is it useful to breeders? Funct. Plant. Biol. 2012, 39, 851-859.
5. Montes, J.M.; Melchinger, A.E.; Reif, J.C. Novel throughput phenotyping platforms in plant genetic studies. Trends Plant. Sci. 2007, 12, 433-436.
6. White, J.W.; Andrade-Sanchez, P.; Gore, M.A.; Bronson, K.F.; Coffelt, T.A.; Conley, M.M.; Feldmann, K.A.; French, A.N.; Heun, J.T.; Hunsaker, D.J.; et al. Field-based phenomics for plant genetics research. Field Crop. Res. 2012, 133, 101-112.
7. Deery, D.; Jimenez-Berni, J.; Jones, H.G.; Sirault, X.; Furbanks, R. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 2014, 5, 349-379.
8. Chapman, S.C.; Merz, T.; Chan, A.; Jackway, P.; Hrabar, S.; Dreccer, M.F.; Holland, E.; Zheng, B.; Ling, T.J.; Jimenez-Berni, J. Pheno-copter: A low altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 2014, 4, 279-301.
9. Araus, J.L.; Cairns, J.E. Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 2014, 19, 52-61.
10. Tuberosa, R. Phenotyping for drought tolerance of crops in the genomics era. Front. Physiol. 2012, 3, doi:10.3389/fphys.2012.00347.
11. Ball, S.T.; Konzak, C.F. Relatioship between grain yield and remotely-sensed data in wheat breeding experiments. Plant Breed. 1993, 110, 277-282.
12. Raun, W.R.; Solie, J.B.; Johnson, G.V.; Stone, M.L.; Lukina, E.V.; Thomason, W.E.; Schepers, J.S. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 2001, 93, 131-138.
13. Kipp, S.; Mistele, B.; Baresel, P.; Schmidhalter, U. High-throughput phenotyping early plant vigour of winter wheat. Eur. J. Agron. 2014, 52, 271-278.
14. Ma, B.L.; Morrison, M.J.; Dwyer, L.M. Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of maize. Agron. J. 1996, 88, 915-920.
15. Richards, R.A.; Condon, A.G.; Rebetzke, G.J. Traits to improve yield in dry environments. In Application of Physiology in Wheat Breeding; Reynolds, M.P., Ortiz-Monasterio, J.I., McNab, A., Eds.; International Maize and Wheat Improvement Center (CIMMYT): Mexico City, Mexico, 2001; pp. 88-100.
16. Blackburn, J.A. Hyperspectral remote sensing of plant pigments. J. Exp. Bot. 2007, 58, 855-867.
17. Zarco-Tejada, P.J.; Gonzalez-Dugo, V.; Berni, J.A.J. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 2012, 117, 322-337.
18. Aparicio, N.; Villegas, D.; Casadesus, J.; Araus, J.L.; Royo, C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 2000, 92, 83-91.
19. Royo, C.; Aparicio, N.; Villegas, D.; Casadesus, J.; Monneveux, P.; Araus, J.L. Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting mediterranean conditions. Int. J. Remote SenS. 2003, 24, 4403-4419.
20. Gutiérrez-Rodríguez, M.; Reynolds, M.P.; Escalante-Estrada, J.A.; Rodríguez-González, M.T. Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions. AusT. J. Agric. Res. 2004, 55, 1139-1147.
21. Gutierrez, M.; Reynolds, M.P.; Raun, W.R.; Stone, M.L.; Klatt, A.R. Spectral water indices for assessing yield in elite bread wheat genotypes under well-irrigated, water-stressed, and high-temperature conditions. Crop Sci. 2010, 50, 197-214.
22. Amani, I.; Fischer, R.A.; Reynolds, M.P. Canopy temperature depression association with yield of irrigated spring wheat cultivars in a hot climate. J. Agron. Crop Sci. 1996, 176, 119-129.
23. Zarco-Tejada, P.J.; Morales, A.; Testi, L.; Villalobos, F.J. Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sens. Environ. 2013, 133, 102-115.
24. Jones, H.G.; Serraj, R.; Loveys, B.R.; Xiong, L.; Wheaton, A.; Price, A.H. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant Biol. 2009, 36, 978-989.
25. Virlet, N.; Lebourgeois, V.; Martinez, S.; Costes, E.; Labbe, S.; Regnard, J.-L. Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints. J. Exp. Bot. 2014, 65, 5429-5442.
26. Zarco-Tejada, P.J.; Miller, J.R.; Mohammed, G.H.; Noland, T.L. Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation. Remote Sens. Environ. 2000, 74, 582-595.
27. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W.; Harlan, J.C. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation. Available online: http://ntrs.nasa.gov/search.jsp?R=19740022555 (accessed on 28 July 2015).
28. Roujean, J.L.; Breon, F.M. Estimating par absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 1995, 51, 375-384.
29. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352.
30. Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 2002, 81, 416-426.
31. Meggio, F.; Zarco-Tejada, P.J.; Nuñez, L.C.; Sepulcre-Canto, G.; Gonzalez, M.R.; Martin, P. Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiological remote sensing indices. Remote Sens. Environ. 2010, 114, 1968-1986.
32. Gamon, J.A.; Peñuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35-44.
33. Hernández-Clemente, R.; Navarro-Cerrillo, R.M.; Suárez, L.; Morales, F.; Zarco-Tejada, P.J. Assessing structural effects on PRI for stress detection in conifer forests. Remote Sens. Environ. 2011, 115, 2360-2375.
34. Zarco-Tejada, P.J.; González-Dugo, V.; Williams, L.E.; Suárez, L.; Berni, J.A.J.; Goldhamer, D.; Fereres, E. A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index. Remote Sens. Environ. 2013, 138, 38-50.
35. Zarco-Tejada, P.J.; Guillén-Climent, M.L.; Hernández-Clemente, R.; Catalina, A.; González, M.R.; Martín, P. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. Forest Meteorol. 2013, 171-172, 281-294.
36. Chappelle, E.W.; Kim, M.S.; McMurtrey Iii, J.E. Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens. Environ. 1992, 39, 239-247.
37. Plascyk, J.A.; Gabriel, F.C. The fraunhofer line discriminator MKII an airborne instrument for precise and standardized ecological luminescence measurement. IEEE Trans. Instrum. Meas. 1975, 24, 306-313.
38. Zarco-Tejada, P.J.; Suarez, L.; Gonzalez-Dugo, V. Spatial resolution effects on chlorophyll fluorescence retrieval in a heterogeneous canopy using hyperspectral imagery and radiative transfer simulation. IEEE Geosci. Remote Sens. Lett. 2013, 10, 937-941.
39. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95-107.
40. Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 1994, 48, 119-126.
41. Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 2001, 76, 156-172.
42. Penuelas, J.; Filella, I.; Lloret, P.; Munoz, F.; Vilajeliu, M. Reflectance assessment of mite effects on apple trees. Int. J. Remote Sens. 1995, 16, 2727-2733.
43. Zarco-Tejada, P.J.; Miller, J.R.; Noland, T.L.; Mohammed, G.H.; Sampson, P.H. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1491-1507.
44. Vogelmann, J.E.; Rock, B.N.; Moss, D.M. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14, 1563-1575.
45. Lichtenthaler, H.K.; Lang, M.; Sowinska, M.; Heisel, F.; Miehé, J.A. Detection of vegetation stress via a new high resolution fluorescence imaging system. J. Plant Physiol. 1996, 148, 599-612.
46. Idso, S.B.; Jackson, R.D.; Pinter, P.J., Jr; Reginato, R.J.; Hatfield, J.L. Normalizing the stress-degree-day parameter for environmental variability. Agric. MeteoroL. 1981, 24, 45-55.
47. Idso, S.B. Non-water-stressed baselines: A key to measuring and interpreting plant water stress. Agric. Meteorol. 1982, 27, 59-70.
48. Moran, M.S.; Clarke, T.R.; Inoue, Y.; Vidal, A. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens. Environ. 1994, 49, 246-263.
49. Marti, J.; Bort, J.; Slafer, G.A.; Araus, J.L. Can wheat yield be assessed by early measurements of normalized difference vegetation index? Ann. Appl. Biol. 2007, 150, 253-257.
50. Farooq, M.; Hussain, M.; Siddique, K.H.M. Drought stress in wheat during flowering and grain-filling periods. Crit. Rev. Plant Sci. 2014, 33, 331-349.
51. Costa, J.M.; Grant, O.M.; Chaves, M.M. Thermography to explore plant-environment interactions. J. Exp. Bot. 2013, 64, 3937-3949.
52. Fischer, R.A.; Rees, D.; Sayre, K.D.; Lu, Z.M.; Condon, A.G.; Larque Saavedra, A. Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Sci. 1998, 38, 1467-1475.
53. Krause, G.H.; Weis, E. Chlorophyll fluorescence as a tool in plant physiology-II. Interpretation of fluorescence signals. Photosynth. Res. 1984, 5, 139-157.
54. Araus, J.L.; Amaro, T.; Voltas, J.; Nakkoul, H.; Nachit, M.M. Chlorophyll fluorescence as a selection criterion for grain yield in durum wheat under mediterranean conditions. Field Crop. Res. 1998, 55, 209-223.
55. Guanter, L.; Zhang, Y.; Jung, M.; Joiner, J.; Voigt, M.; Berry, J.A.; Frankenberg, C.; Huete, A.R.; Zarco-Tejada, P.; Lee, J.E.; et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA 2014, 111, 1327-1333.
56. Peñuelas, J.; Isla, R.; Filella, I.; Araus, J.L. Visible and near-infrared reflectance assessment of salinity effects on barley. Crop Sci. 1997, 37, 198-202.
57. Reynolds, M.P.; Rajaram, S.; Sayre, K.D. Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Sci. 1999, 39, 1611-1621.
58. Lobos, G.A.; Matus, I.; Rodriguez, A.; Romero-Bravo, S.; Araus, J.L.; Del Pozo, A. Wheat genotypic variability in grain yield and carbon isotope discrimination under mediterranean conditions assessed by spectral reflectance. J. Integr. Plant Biol. 2014, 56, 470-479.