Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley

European Journal of Agronomy

Volumn 78, Issue , 2016, Pages 44-59

  Rischbeck, Pablo ^a   Elsayed, Salah ^b   Mistele, Bodo ^c   Barmeier, Gero ^a   Heil, Kurt ^a   Schmidhalter, Urs ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b Menofia Governorate   (Egypt)

^c SOUTH WESTPHALIA UNIVERSITY OF APPLIED SCIENCES   (Germany)

Author keywords

Abiotic stress; Data fusion; High throughput phenotyping; Multi annual; Multiple linear regression; Partial least square; Phenomics; Yield modelling

Indexed keywords

ABIOTIC FACTOR; BARLEY; CANOPY ARCHITECTURE; CROP IMPROVEMENT; CROP YIELD; CULTIVAR; DATA ASSIMILATION; DROUGHT STRESS; HEIGHT; LEAST SQUARES METHOD; MULTIPLE REGRESSION; PHENOTYPE; PREDICTION; REMOTE SENSING; SPECTRAL ANALYSIS; SPRING (SEASON); THERMAL ALTERATION; YIELD RESPONSE;

HORDEUM;

EID: 84966339697     PISSN: 11610301     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eja.2016.04.013     Document Type: Article

References (69)

1. Agüera Vega F., Carvajal Ramírez F., Pérez Saiz M., Orgaz Rosúa F. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosyst. Eng. 2015, 132:19-27.
2. 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.
3. Bradford K.J., Hsiao T.C. Physiological responses to moderate water stress, in Encyclopedia of Plant Physiology. Physiological Plant Ecology II, Water Relations and Carbon Assimilation 1982, vol. 12B:263-324. Springer-Verlag, New York, Heidelberg, Berlin.
4. 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. 2000, 76(2):156-172.
5. Calderón R., Navas-Cortés C., Lucena C., Zarco-Tejada P.J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 2013, 139:231-245.
6. Carlson T.N., Ripley D.A. On the relation between NDVI, fractional vegetation cover and leaf area index. Remote Sens. Environ. 1997, 62(3):241-252.
7. Cho M.A., Skidmore A.K. A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sens. Environ. 2006, 101:181-193.
8. Clevers J.G., De Jong S.M., Epema G.F., Van der Meer F.D., Bakker W.H., Skidmore A.K., Scholte K.H. Derivation of the red edge index using the MERIS standard band setting. Int. J. Remote Sens. 2002, 23(16):3169-3184.
9. Comar A., Burger P., de Solan B., Baret B., Daumard F., Hanocq J.F. A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Funct. Plant Biol. 2012, 39:914-924.
10. Deutscher Wetterdienst, 2015. Deutscher Klimaatlas.
11. Efron B., Tibshirani R. An Introduction to the Bootstrap 1993, Chapman and Hall, New York, London.
12. Erdle K., Mistele B., Schmidhalter U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 2011, 124:74-84.
13. Erdle K., Mistele B., Schmidhalter U. Spectral high-throughput assessments of phenotypic differences in biomass and nitrogen partitioning during grain filling of wheat under high yielding Western European conditions. Field Crops Res. 2013, 141:16-26.
14. Fu Y., Yang G., Wang J., Song X., Feng H. Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Comput. Electron. Agric. 2013, 100:51-59.
15. Gutierrez, M., Reynolds, M.P., Raun, W.R., Stone, M.L., Klatt, A.R., 2010. Spectral water indices for assessing yield in elite bread wheat genotypes under well-irrigated, water-stressed, and high-temperature conditions.
16. Gutierrez-Rodriguez M., Reynolds M.P., Escalante-Estrada J.A., Rodriguez-Gonzalez 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.
17. Guyot G., Baret F., Major D.J. High spectral resolution: determination of spectral shifts between the red and the near infrared. Int. Arch. Photogramm. Remote Sens. 1988, 11:750-760.
18. 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(3):337-352.
19. Hansen P.M., Schjoerring J.K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 2003, 86(4):542-553.
20. Hansen P.M., Jorgensen J.R., Thomsen A. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. J. Agric. Sci. 2002, 139:307-318.
21. Heil K., Schmidhalter U. Characterisation of soil texture variability using the apparent soil electrical conductivity at a highly variable site. Comput. Geosci. 2012, 39:98-110.
22. Herrmann I., Pimstein A., Karnieli A., Cohen Y., Alchanatis V., Bonfil D.J. Assessment of leaf area index by the red edge inflection point derived from VENμS bands. Proceedings of the 'Hyperspectral 2010 Workshop 2010.
23. Honkavaara E., Saari H., Kaivosoja J., Pölönen I., Hakala T., Litkey P., Mäkynen J., Pesonen L. Processing and assessment of spectrometric, stereoscopic Imagery collected using a lightweight UAV spectral camera for Precision Agriculture. Remote Sens. 2013, 5:5006-5039. 10.3390/rs5105006.
24. IBM Corp 2012 IBM SPSS Statistics for Windows Version 21 0. Armonk, NY : IBM Corp.
25. Idso S.B., Jackson R.D., Pinter P.J., Reginato R.J., Hatfield J.L. Normalizing the stress degree-day parameter for environmental variability. Agric. Meteorol. 1981, 24:45-55.
26. Jackson R.D., Idso S.B., Reginato R.J., Pinter P.J. Canopy temperature as a crop water stress indicator. Water Resour. Res. 1981, 17:1133-1138.
27. Jones H.G. Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Adv. Bot. Res. 2004, 41:107-163.
28. Kipp S., Mistele B., Schmidhalter U. Identification of stay-green and early-senescence phenotypes in high-yielding winter wheat and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques. Funct. Plant Biol. 2014, 41:227-235.
29. Laurent A., Loyce C., Makowski D., Pelzer E. Using site-specific data to estimate energy crop yield. Environ. Model. Softw. 2015, 74:104-113.
30. Li F., Martin L., Jia L., Miao Y., Yu Z., Kopp W., Bareth G., Chen X., Zhang F. Estimating N status of winter wheat using a handheld spectrometer in the North China plain. Field Crops Res. 2008, 106(1):77-85.
31. Li Z., Jin X., Zhao C., Wang J., Xu X., Yang G., Li C., Shen J. Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing. Eur. J. Agron. 2015, 71:53-62.
32. Lichtenthaler H.K. Vegetation stress: an Introduction to the stress concept in plants. J. Plant Physiol. 1996, 148(1-2):4-14.
33. 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.
34. Lucas R., Bunting P., Paterson M., Chisholm L. Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sens. Environ. 2008, 112(5):2088-2103.
35. 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.
36. Mevik B.H., Wehrens R. The pls package: principal component and partial least squares regression in R. J. Stat. Softw. 2007, 18(2):1-24.
37. Mistele B., Schmidhalter U. Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Res. 2008, 106:94-103.
38. Mistele B., Schmidhalter U. Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agron. J. 2010, 102(2):499-506.
39. Mistele B., Bredemeier C., Gutser R., Schmidhalter U. Spectral detection of nitrogen status, biomass and yield of field-grown maize plants. Programme Book of the Joint Conference of EPCA-EPCLF 2003, 497-498. Wageningen Academic Publishers, EPCA Berlin, 15-19.6.03. A. Werner, A. Jarfe (Eds.).
40. Mistele B., Gutser R., Schmidhalter U. Validation of Field-Scaled Spectral Measurements of the Nitrogen Status in Winter Wheat. Programm book of the joint conference ICPA 2004, 1187-1195.
41. Mistele B., Elsayed S., Schmidhalter U. Assessing water status in wheat under field conditions using laser induced chlorophyll fluorescence and hyperspectral measurements. 11th International Conference on Precision Agriculture 2012.
42. Montes J.M., Technow F., Dhillon B.S., Mauch F., Melchinger A.E. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res. 2011, 121:268-273.
43. Mouazen A.M., Alhwaimel S.A., Kuang B., Wayne T. Multiple online soil sensors and data fusion approach for delineation of water holding capacity zones for site-specific irrigation. Soil Tillage Res. 2014, 143:95-105.
44. Nguyen T.A., Lee B.W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur. J. Agron. 2006, 24:349-356.
45. Olufayo A., Baldy C., Ruelle P. Sorghum yield, water use and canopy temperatures under different levels of irrigation. Agric. Water Manage. 1996, 30:77-90.
46. Peñuelas J., Filella I., Serrano L. The reflectance at the 950-970 nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14:1887-1905.
47. Pinter P.J., Fry K.E., Guinn G., Mauney J.R. Infrared thermometry: a remote sensing technique for predicting yield in water-stressed cotton. Agric. Water Manage. 1983, 6:385-395.
48. R Core Team, 2014 R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
49. Rebetzke G.J., Chenu K., Biddulph B., Moeller C., Deery D.M., Rattey A.R., Bennett D., Barrett-Lennard E.G., Mayer J.E. A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct. Plant Biol. 2013, 40(1):1-13. 10.1071/FP12180.
50. Rischbeck P., Baresel P., Elsayed S., Mistele B., Schmidhalter U. Development of a diurnal dehydration index for spring barley phenotyping. Funct. Plant Biol. 2014, 41(12):1249-1260. FP14069 (published online). 10.1071/FP14069.
51. Overview and Recent Advances in Partial Least Squares. In Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J., 2006. editors. 'Subspace, Latent Structure and Feature Selection Techniques', Lecture Notes in Computer Science, 34-51.
52. Rostamza M., Chaichi M.R., Jahansouz M.R., Alimadadi A. Forage quality, water use and nitrogen utilization efficiencies of pearl millet (Pennisetum americanum L.) grown under different soil moisture and nitrogen levels. Agric. Water Manage. 2011, 98:1607-1614.
53. 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.
54. Schmidhalter U., Evéquoz M., Camp K.H., Studer C. Sequence of drought response of maize seedlings in drying soil. Physiol. Plant. 1998, 104:159-168.
55. Schmidhalter U., Glas J., Heigl R., Manhart R., Wiesent S., Gutser R., Neudecker E. Application and testing of a crop scanning instrument - field experiments with reduced crop width, tall maize plants and monitoring of cereal yield. 3rd. European Conference for Precision Agriculture 2001, 953-958.
56. Serrano L., Gonzalez-Flor C., Gorchs G. Assessment of grape yield and composition using the reflectance based water index in mediterranean rainfed vineyards. Remote Sens. Environ. 2011, 118:249-258.
57. Sharabian V.R., Noguchi N., Ishi K. Significant wavelengths for prediction of winter wheat growth status and grain yield using multivariate analysis engineering in agriculture. Environ. Food 2014, 7:14-21.
58. Siddique M.R., Hamid A., Islam M.S. Drought stress effects on water relations of wheat. Bot. Bull. Acad. Sin. 2000, 41:35-39.
59. 2 and drought effects on leaf gas exchange properties of barley agriculture. Ecosyst. Environ. 2011, 144:390-404.
60. Weber V.S., Araus J.L., Cairns J.E., Sanchez C., Melchinger A.E., Orsini E. Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Res. 2012, 128:82-90.
61. White E.M., Wilson F.E.A. Responses of grain yield, biomass and harvest index and their rates of genetic progress to nitrogen availability in ten winter wheat varieties. Irish J. Agric. Food Res. 2006, 45:85-101.
62. 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., Jenks M.A., Kimball B.A., Roth R.L., Strand R.J., Thorp K.R., Wall G.W., Wang G. Field-based phenomics for plant genetics research. Field Crops Res. 2012, 133:101-112.
63. Winterhalter L., Mistele B., Jampatong S., Schmidhalter U. High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur. J. Agron. 2011, 35:22-32.
64. Winterhalter L., Mistele B., Schmidhalter U. Evaluation of active and passive sensor systems in the field to phenotype maize hybrids with high-throughput. Field Crops Res. 2013, 154:236-245.
65. Wright, R., Lucey, P., Crites, S., Horton, K., Wood, M., Garbeil, H., 2013. BBM/EM design of the thermal hyperspectral imager: an instrument for remote sensing of earth's surface, atmosphere and ocean, from a microsatellite platform.
66. Zarco-Tejada P.J., Miller J.R., Noland T.L., Mohammed G.H., Sampson P.H. Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2001, 39:1491-1507.
67. Zarco-Tejada P.J., Berjón A., López-Lozano R., Miller J.R., Martín P., Cachorro V., González M.R., de Frutos A. Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row structured discontinuous canopy. Remote Sens. Environ. 2005, 99:271-287.
68. Zhao T., Komatsuzaki M., Okamoto H., Sakai K. Cover crop nutrient and biomass assessment system using portable hyperspectral camera and laser distance sensor. Eng. Agric. Environ. Food 2010, 3:105-112.
69. Zhao Y., Chen X.P., Cui Z.L., Lobell D.B. Using satellite remote sensing to understand maize yield gaps in the North China Plain. Field Crops Res. 2015, 138:31-42.