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Sweet Corn Yield Simulation Using Normalized Difference Vegetation Index and Leaf Area Index

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Języki publikacji
EN
Abstrakty
EN
The authors determined the accuracy and reliability of yielding models by using the values of two differently obtained indices – the leaf area index (LAI) obtained through direct surface measurements, and the normalized difference vegetation index (NDVI) obtained through spatial remote sensing of crops. The study based on the drip-irrigated sweet corn yielded the data obtained in the field experiment held in the semi-arid climate on darkchestnut soil in the South of Ukraine. The suitability of the LAI and NDVI for the simulation of sweet corn yields was estimated by the regression analysis of the yielding data by correlation (R) and determination (R2) coefficients. Additionally, mathematical models for the crop yields estimation based on the regression analysis were developed. It was determined that LAI is a more suitable index for the crop yield prediction: the R2 value was 0.92 and 0.94 against 0.85 for the NDVI-based models.I It was determined that it is better to use the LAI values obtained at the stage of flowering, when R2 averaged to 0.94, and the NDVI-based models does not depend on the crop stage (the R2 was 0.85 both for the flowering and ripening stages of the plant development). The combined NDVI-LAI model showed that there is no necessity in the complication of the LAI-based model through introduction of the remotely sensed index because of insignificant improvement in the performance (R2 was 0.94 and 0.92).
Rocznik
Strony
228--236
Opis fizyczny
Bibliogr. 27 poz., tab.
Twórcy
  • Department of Scientific and Innovative Activity, Transfer of Technologies and Intellectual Property, Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483, Kherson, Ukraine, pavel.likhovid@gmail.com
Bibliografia
  • 1. Aparicio N., Villegas D., Casadesus J., Araus J.S., Royo C. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J., 92(1), 83-91. http://doi.org/10.2134/agronj2000.92183x
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  • 5. Bréda N.J.J. 2003. Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J. Experiment. Bot., 54(392), 2403–2417. http://doi.org/10.1093/jxb/erg263
  • 6. Carlson T.N., Ripley D.A. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ., 62(3), 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1
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  • 9. Gamon J.A., Field C.B., Goulden M.I., Griffin K.L., Hartley A.E., Joel G., Penuetas J., Valentini R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl., 5(1), 28-41. https://doi.org/10.2307/1942049
  • 10. Hird J.N., McDermid G.J. 2009. Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sens. Environ., 113(1), 248-258. http://doi.org/10.1016/j.rse.2008.09.003
  • 11. Horie T., Yajima M., Nakagawa H. 1992. Yield forecasting. Agric. Syst., 40(1-3), 211-236. http://doi.org/10.1016/0308-521X(92)90022-G
  • 12. Huang J., Tian L., Liang S., Ma H., Becker-Reshef I., Huang Y., Su W., Zhang X., Zhu D., Wu W. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agric. For. Meteorol., 204, 106-121. http://doi.org/10.1016/j.agrformet.2015.02.001
  • 13. Huang J., Wang X., Li X., Tian H., Pan Z. 2013. Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA‘s-AVHRR. PloS One, 8(8), e70816. https://doi.org/10.1371/journal.pone.0070816
  • 14. Jiang D., Yang X., Clinton N., Wang N. 2004. An artificial neural network model for estimating crop yields using remotely sensed information. Intern. J. Remote Sens. 25(9): 1723-1732. http://doi.org/10.1080/0143116031000150068
  • 15. Kouadio L., Newlands N.K., Davidson A., Zhang Y., Chipanshi A. 2014. Assessing performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sens., 6(10), 10193-10214. https://doi.org/10.3390/rs61010193
  • 16. Lewis J.E., Rowland J.J., Nadeau A. 1998. Estimating maize production in Kenya using NDVI: some statistical considerations. Intern. J. Remote Sens., 19(13), 2609-2617. http://doi.org/10.1080/014311698214677
  • 17. Liu Q., Huete A. 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE T. Geosci. Remote Sens., 33(2), 457-465. https://doi.org/10.1109/TGRS.1995.8746027
  • 18. Maas S.J. 1988. Use of remotely-sensed information in agricultural crop growth models. Ecol. Model., 41(3-4), 247-268. http://doi.org/10.1016/0304-3800(88)90031-2
  • 19. Myneni R.B., Hall F.G., Sellers P.J., Marshak A.L.. 1995. The interpretation of spectral vegetation indexes. IEEE T. Geosci. Remote Sens., 33(2), 481486. http://doi.org/10.1109/36.377948
  • 20. Prasad A.K., Chai L., Singh R.P., Kafatos M. 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. Intern. J. Appl. Earth Observ. Geoinform., 8(1), 26-33. https://doi.org/10.1016/j.jag.2005.06.002
  • 21. Quarmby N.A., Milnes M., Hindle T.L., Silleos N. 1993. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Intern. J. Remote Sens., 14(2), 199210. https://doi.org/10.1080/01431169308904332
  • 22. Rasmussen M.S. 1992. Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR. Intern. J. Remote Sens., 13(18), 3431-3442. http://doi.org/10.1080/01431169208904132
  • 23. Sellers P.J. 1985. Canopy reflectance, photosynthesis and transpiration. Intern. J. Remote Sens., 6(8), 13351372. http://doi.org/10.1080/01431168508948283
  • 24. Son N.T., Chen C.F., Chen C.R., Chang L.Y., Duc H.N., Nguyen L.D. 2013. Prediction of rice crop yield using MODIS EVI-LAI data in the Mekong Delta, Vietnam. Intern. J. Remote Sens., 34(20), 7275-7292. http://doi.org/10.1080/01431161.2013.818258
  • 25. Thomason W.E., Phillips S.B., Raymond F.D. 2007. Defining useful limits for spectral reflectance measures in corn. J. Plant Nutr., 30(8), 1263-1277. http://doi.org/10.1080/01904160701555176
  • 26. Triola M.F. 2013. Elementary statistics using Excel. Pearson.
  • 27. Ushkarenko V.O., Kokovikhin S.V., Holoborodko S.P., Vozhehova R.A. 2014. The methodology of the field experiment (irrigated agriculture): the textbook. Hrin D.S., Kherson.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-78302bb6-8d35-4bc6-aa49-ce1ae650a830
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