Estimation of winter wheat (Triticum aestivum L.) leaf pigments content using Landsat‑8 imagery

Document Type : Research Paper

Authors

1 Student/Sharekord university

2 Pedology Department, Faculty of Agriculture, Shahrekord University, Shahrekord, IRAN.

3 Associate Professor, Agronomy Department., Faculty of Agriculture, Shahrekord University

4 Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.

Abstract

Background and objectives: Leaf chlorophyll content in plants indicates their health, physiological status, their photosynthetic activity and nitrogen content. Monitoring pigment content of crop tissues helps identification of plant nutrition and environmenta stresses before serious damages to crop and yield. This study was conducted to estimate the leaf pigments content of winter wheat (Triticum aestivum L.) as one of the most important crops using Landsat 8 satellite data and statistical models in Shahrekord county, Chaharmahal and Bakhtiari province in 2017.
Materials and methods: For this purpose, eight fields under winter wheat cultivation with an area between 10 to 60 hectares throughout Shahrekord county were considered. The location of 120 sampling units was randomly and determined in fields using ground positioning system (GPS). The sampling units were 30 × 30 m squares according to Landsat pixels. In each unit 5 plots (0.5 × 0.5 m) were considered, four plots in the corners and one in the center of the unit. Crop sampling and passing over of satellite were synchoronous. The samples were transferred to the laboratory and their chlorophyll and carotenoid content were measured. The corresponded Landsat 8 data were processed and and vegetation indices were calculated. Simple and multiple stepwise linear regression methods were used to obtain models for estimating chlorophylls and carotenoids content of wheat leaves.
Results: The results showed that among the selected indices, PSRI index for estimating chlorophyll a content (R2 = 0.408 and RMSE= 8.38 µg.cm-2), chlorophyll b (R2 = 0.400 and RMSE= 3.69 µg.cm-2) and total chlorophyll (R2 = 0.500 and RMSE= 10.82 µg.cm-2), CRI index (R2 = 0.480 and RMSE= 2.92 µg.cm-2) for estimating leaf carotenoid content and SIPI index (R2 = 0.603 and RMSE = 0.17 µg.cm-2) for estimating carotenoid to chlorophyll a ratio had the best performance in simple linear regression. Models based on multiple stepwise linear regression estimated chlorophyll a content with R2 = 0.557 and RMSE = 7.73 µg.cm-2, chlorophyll b with R2 = 0.471 and RMSE = 3.69 µg.cm-2, total chlorophyll with R2 = 0.611 and RMSE = 10.10 µg.cm-2, carotenoids with R2 = 0.50 and RMSE = 2.01 µg.cm-2 and carotenoid to chlorophyll a ratio with R2 = 0.756 and RMSE = 0.12 µg.cm-2. PSRI and CVI indices for estimating chlorophyll a and total chlorophyll content, PSRI index for estimating chlorophyll b content, CRI and TCI / OSAVI indices for estimating carotenoid content and SIPI, GNDVI, EVI, CIgreen, TCARI and OSAVI for carotenoid to chlorophyll a ratio of wheat leaf were the most effective indices in a stepwise multiple linear regression models. In our study, estimation of chlorophyll a, total, carotenoid content and carotenoid to chlorophyll a ratio based on stepwise multiple linear regression was superior to simple linear regression models. Estimation of chlorophyll b content was the same in both methods.
Conclusion: The use of vegetation indices derived from Landsat 8 data makes it possible to estimate the content of wheat leaf pigments with relatively good results in the study area. Such information is important for farmers to initially monitor the quality and quantity of production and leads to a practical and efficient planning in the fertilization process.

Keywords

References

  1.  Abassi, M., 2009. Investigation of the spectral signature of forest species leaf: Fagus orientalis, Quercus castaneifolia, Carpinus betulus, Alnus subcordata, Parotia persica using field spectroradiometry. PhD thesis forestry and forest economic, Faculty of Natural Resources, University of Tehran, Iran. (In Persian)

    1. Bannari, A., Khurshid, K.S., Staenz, K., and Schwarz, J.W. 2007. A Comparison of hyperspectral chlorophyll indices for wheat crop chlorophyll content estimation using laboratory reflectance measurements. IEEE Trans Geosci Remote Sens. 45: 10. 3063-3074.
    2. Blackburn, G.A. 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ. 66: 3. 273-285.
    3. Blackmer, T.M., Shepers, J.S., and Varvel, G.V. 1994. Light reflectance compared with other nitrogen stress measurements in corn leaves. Agron J. 86: 6. 934-938.
    4. Broge, N.H., and Mortensen, J.V. 2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sens. Environ. 81: 1. 45-57.
    5. Broge, N.H. and Leblanc, E. 2000. Comparing predictive power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76: 2. 156-172.
    6. Carmona, F., Rivas, R., and Fonnegra, D.C. 2015. Vegetation Index to estimate chlorophyll content from multispectral remote sensing data. Eur. J. Remote Sens. 48: 1. 319-326.
    7. Chappelle, E.W., Kim, M.S., and McMurtrey III, J.E. 1992. 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. 39: 3.239-247.
    8. Clevers, J.G.P.W. 1986. The application of a vegetation index in correcting the infrared reflectance for soil background. Int. Arch. Photogramm. Rem. Sens, Balkema, Rotterdam, Boston. 26: 1. 221-
    9. Clevers, J.G.P.W., Kooistra, L., and Brande M.M.M. 2017. Using sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens. 9: 5. 1-15. Doi:10.3390/rs9050405.
    10. Cramer, W., and Field, C.B. 1999. The potsdam NPP model intercomparison. Glob Change Biol. 5 :1. 1-15.
    11. Croft, H., Arabian, J., Chen, J.M., Shang, J., and Liu, J. 2019. Mapping within‑field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat‑8 imagery. Precis Agric. 21: 4. 856-880. https://doi.org/10.1007/s11119-019-09698-y.
    12. Croft, H., Chen, J.M., and Zhang, Y. 2014. The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecol Complex. 17: 119-
    13. Dash, J., and Curran, P.J. 2004. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 25: 23. 5403-5413.
    14. Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Brown de Colstoun, E., and McMurtrey III, J.E. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74: 229-239.
    15. Flexas, J.L., Briantais, J.M., Cerovic, Z., Medrano, H., and Moya, I. 2000. Steady-state and maximum chlorophyll fluorescence responses to water stress in grapevine leaves. A new remote sensing system. Remote Sens. Environ. 73: 283-297.
    16. Gamon, J.A., and Surfus, J.S. 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 143: 1. 105-117.
    17. Gitelson, A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161: 165-173.
    18. Gitelson, A.A. and Merzlyak, M.N. 1997. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 18: 2691-2697.
    19. Gitelson, A.A., Gritz, Y., and Merzlyak, M.N. 2003a. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160: 3. 271-282.
    20. Gitelson, A.A., Kaufman, J.Y., and Merzlyac, M.N. 1996. Use of a Green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58: 3. 289-298.
    21. Gitelson, A.A., Vina, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G.P., and Leavitt, B. 2003b. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys Res Lett. 30: 5.1-4.
    22. Gitelson, A.A., Vina, A., Ciganda, V., Rundquist, D.C., and Arkebauer, T.J. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys Res Lett. 32: 8. 1-4.
    23. Gitelson, A.A., Zur, Y., Chivkunova, O.B., and Merzlyak, M.N. 2002 Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol. 75: 3. 272-281.
    24. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., and Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81: (2-3). 416-426.
    25. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., and Strachan, I.B. 2004. 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. 90: 337-352.
    26. Haboudane, D., Tremblay, N., Miller, J.R., and Vigneault, P. 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Trans Geosci Remote Sens. 46: 2. 423-437.
    27. Han, S., Hendrickson, L.L., and Ni, B. 2002. Comparison of satellite and aerial imagery for detecting leaf chlorophyll content in corn. Trans ASAE. 45: 4. 1229-1239.
    28. Hatfield, J.L., Gitelson, A.A., Schepers, J.S., and Walthall, C.L. 2008. Application of spectral remote sensing for agronomic decisions. Agron J. 100: 117-131.
    29. Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25: 3. 295-309.
    30. Huete, A.R., Liu, H.Q., Batchily, K., and Leeuwen, W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59: 3. 440-451.
    31. Kim, M.S. 1994. The use of narrow spectral bands for improving remote sensing estimation of fractionally absorbed photosynthetically active radiation (fAPAR). Masters Thesis. Department of Geography, University of Maryland, College Park, MD.
    32. Kooistra, L., and Clevers, J.G.P.W. 2016. Estimating potato leaf chlorophyll content using ratio vegetation indices. Remote Sens Lett. 7: 611-620.
    33. Le Maire, G., Francois, C., and Dufrene, E. 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 89: 1-28.
    34. Li, F., Miao, Y., Hennig, S.D., Gnyp, M.L., Chen, X., Jia, L., and Bareth, G. 2010. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis Agric. 11: 4. 335-357.
    35. Lichtenthaler, H.K. 1987. Chlorophyll and carotenoids: Pigments of photosynthetic biomembranes. Methods. Enzymol. 148: 350-387.
    36. Lichtenthaler, H.K., and Buschmann, C. 2001. Chlorophylls and carotenoids: Measurement and characterization by UV–VIS spectroscopy. Current protocols in food analytical chemistry (pp. F4.3.1-F4.3.8). New York: John Wiley and Sons.
    37. Liu, J. and Moore, J.M. 1990. Hue image RGB colour composition. A simple technique to suppress shadow and enhance spectral signature. Int. J. Remote Sens. 11: 8. 1521-1530.
    38. Merzlyak, M.N., Gitelson, A.A., Chivkunova O.B., and Rakitin, V.Y. 1999. Non-destructive optical detection of leaf senescence and fruit ripening. Physiol Plant. 106: 135-141.
    39. Miraglio, T., Adeline, K., Huesca, M., Ustin, S., and Briottet, X. 2020. Monitoring LAI, chlorophylls, and carotenoids content of awoodland savanna using hyperspectral imagery and 3d radiative transfer modeling. Remote Sens. 12: 1. 1-28.
    40. Mosleh Ghahfarokhi, Z. 2016. Soil digital mapping, land suitability and optimization cultivation model for major products plains of Shahrekord. PhD thesis pedology, Faculty of Agriculture, University of Shahrekord, Iran. (In Persian)
    41. Nagy, A., Feher, J., and Tamas, J. 2018. Wheat and maize yield forecasting for the Tisza River Catchment using MODIS NDVI time series and reported crop statistics. Comput. Electron. Agric. 151: 10. 41-49.
    42. Nguyen, H., Kim, J., Nguyen, A., Shin, J.C., and Lee, B. 2006. Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of Rice. Precis Agric. 7: 4. 249-264.
    43. Pearson, R.L. and Miller, L.D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Remote Sens. Environ. 8: 1348-1355.
    44. Penuelas, J., Baret, F., and Filella, I. 1995. Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica. 31: 2. 221-230.
    45. Penuelas, J., Gamon, J.A., Freeden, A.L., Merino, J. and Field, C.1994. Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sens. Environ. 48: 2. 135-146.
    46. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., and Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sens. Environ. 48: 2. 119-126.
    47. Ranjan, A.K. and Parida, B.R. 2020. Estimating biochemical parameters of paddy using satellite and near-proximal sensor data in Sahibganj Province, Jharkhand (India). Remote Sens App Soc Environ. 18: 1. 1-12.
    48. Rondeaux, G., Steven, M., and Baret, F. 1996. Optimization of soil- adjusted vegetation indices. Remote Sens. Environ. 55: 2. 95-107.
    49. Roujean, J.L., and Breon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 51:3. 375-384.
    50. Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Pp. 309-317. In: S.C. Freden, E.P. Mercanti, and M. Becker (eds) Third Earth Resources Technology Satellite–1 Syposium. Volume I: Technical Presentations, NASA SP-351, NASA, Washington, D.C.
    51. Sage, R.F., Pearcy, R.W., and Seemann, J.R. 1987. The nitrogen use efficiency of C3 and C4 plants III. Leaf nitrogen effects on the activity of carboxylating enzymes in Chenopodium album (L.) and Amaranthus retroflexus (L.). J. Plant Physiol. 85: 2. 355-359.
    52. Sims, D.A., and Gamon, J.A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81: 2-3.337-354.
    53. Sinclair, T.R., and Rufty, T.W. 2012. Nitrogen and water resources commonly limit crop yield increases, not necessarily plant genetics. Glob Food Sec. 1: 2. 94-98.
    54. Verrelst, J., Camps-Valls, G., Munoz-Mari, J., Rivera, J.P., Veroustraete, F., Clevers, J.G., and Moreno, J. 2015. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties, A review. ISPRS J. Photogramm. Remote Sens. 108: 273-290.
    55. Vincini, M., Frazzi, E., and D’Alessio, P. 2008. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis Agric. 9: 303-319.
    56. Wu, C., Niu, Z., Tang, Q., and Huang, W. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric. For. Meteorol. 148: 8-9. 1230-1241.

    Xue, J., and Su, B. 2017. Significant 

Journal of Crop Production
Volume 14, Issue 3
October 2021
Pages 85-106

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