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Integration Remote Sensing and Meteorological Data to Monitoring Plant Phenology and Estimation Crop Coefficient and Evapotranspiration

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The water requirements of the wheat crop are represented by the actual evapotranspiration, which depends on the meteorological data of the study area and the amount of water consumed during the season. Estimation of crop coefficients (Kc) and evapotranspiration (ETc) using remote sensing data is essential for decision-making regarding water management in irrigated areas in arid and semi-arid large-scale areas. This research aims to estimate the crop coefficient calculated from remote sensing data and the actual evapotranspiration values for the crop. The FAO Penman-Monteith equation has been used to estimate the reference evapotranspiration from meteorological data. Linear regression analysis was applied by developing prediction equations for the crop coefficient for different growth stages of comparing with the vegetation cover index (NDVI). The results showed that (R2 = 0.98) between field crop coefficient and crop coefficient predicted from (Kc = 2.0114 NDVI-0.147) in addition to (RMSE = 0.92 and (d = 0.97).
Rocznik
Strony
325--335
Opis fizyczny
Bibliogr. 37 poz., rys.
Twórcy
  • College of Engineering, Al-Qasim Green University, 8, Al Qasim, Iraq
  • College of Engineering, Al-Qasim Green University, 8, Al Qasim, Iraq
  • College of Engineering, Al-Qasim Green University, 8, Al Qasim, Iraq
autor
  • National Council for Scientific Research, Beirut, Lebanon
Bibliografia
  • 1. Abdalkadhum A.J., Salih M.M., Jasim O.Z. 2020. Combination of visible and thermal remotely sensed data for enhancement of Land Cover Classifi cation by using satellite imagery’, in IOP Conference Series: Materials Science and Engineering. IOP Publishing, 12226.
  • 2. Acharya B., Sharma V. 2021. Comparison of satellite driven surface energy balance models in estimating crop evapotranspiration in semi-arid to arid inter-mountain region. Remote Sensing. DOI: 10.3390/rs13091822
  • 3. Adamala S., Rajwade Y.A., Reddy Y.V.K. 2016. Estimation of wheat crop evapotranspiration using NDVI vegetation index. Journal of Applied and Natural Science, 8(1), 159–166.
  • 4. Al-Mansoori T., Abdalkadhum A., Al-Husainy A.S. 2020. A GIS-Enhanced pavement management system: a case study in Iraq. Journal of Engineering Science and Technology, 15(4), 2639–2648.
  • 5. Alface A.B. et al. 2019. Sugarcane spatial-temporal monitoring and crop coefficient estimation through NDVI. Revista Brasileira de Engenharia Agrícola e Ambiental. SciELO Brasil, 23, 330–335.
  • 6. Ali Z.A., Hassan D.F., Mohammed R.J. 2021. Effect of irrigation level and nitrogen fertilizer on water consumption and faba bean growth’, in IOP Conference Series: Earth and Environmental Science. IOP Publishing, 12043.
  • 7. Aljanbi A.J.A., Dibs H., Alyasery B.H. 2020. Interpolation and statistical analysis for evaluation of global earth gravity models based on GPS and orthometric heights in the middle of Iraq. Iraqi Journal of Science, 1823–1830.
  • 8. Allen R.G., Pereira L.S., Raes, Dirk, et al. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
  • 9. Allen R.G. et al. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Applications. Journal of irrigation and drainage engineering. American Society of Civil Engineers, 133(4), 395–406.
  • 10. Campos I. et al. 2010. Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.). Agricultural Water Management. Elsevier, 98(1), 45–54.
  • 11. Dingre S.K., Gorantiwar S.D., Kadam S.A. 2021. Correlating the field water balance derived crop coefficient (Kc) and canopy reflectance-based NDVI for irrigated sugarcane. Precision Agriculture. Springer, 1–20.
  • 12. Er-Raki S. et al. 2007. Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region’, Agricultural water management. Elsevier, 87(1), 41–54.
  • 13. Evett S.R. et al. 2012. Can weighing lysimeter ET represent surrounding field ET well enough to test flux station measurements of daily and sub-daily ET? Advances in Water Resources. Elsevier, 50, 79–90.
  • 14. Foken T., Napo C.J. 2008. Micrometeorology, Springer.
  • 15. Fraenkel A.P. 1986. FAO irrigation and drainage paper 43: water lifting. Food and Agriculture Organization of the United Nations, Rome.
  • 16. Gibson J.J. 2002. Short-term evaporation and water budget comparisons in shallow Arctic lakes using non-steady isotope mass balance. Journal of Hydrology. Elsevier, 264(1–4), 242–261.
  • 17. Gilabert M.A. et al. 2002. A generalized soil-adjusted vegetation index. Remote Sensing of environment. Elsevier, 82(2–3), 303–310.
  • 18. Guan S. et al. 2019. Assessing correlation of highresolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing. Multidisciplinary Digital Publishing Institute, 11(2), 112.
  • 19. Gutman G.G. 1991. Vegetation indices from AVHRR: An update and future prospects’, Remote Sensing of environment. Elsevier, 35(2–3), 121–136.
  • 20. Hassan D.F., Jafaar A.A., Mohamm R.J. 2019. Effect of irrigation water salinity and tillage systems on some physical soil properties. Iraqi Journal of Agricultural Sciences. College of Agriculture, University of Baghdad, 50(Special Issue), 42–47.
  • 21. Hassan D.F., Ati A.S., Neima A.S. 2021. Calibration and Evaluation of Aquacrop for Maize (Zea Mays L.) under Different Irrigation and Cultivation Methods. Journal of Ecological Engineering, 22(10), 192–204. https://doi.org/10.12911/22998993/142123
  • 22. Hassan M.A. et al. 2019. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform, Plant science. Elsevier, 282, 95–103.
  • 23. Hubbard B.K.A.K.K. 2013. Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index. Remote Sensing, 1588–1602. https://doi.org/10.3390/rs5041588
  • 24. Hunsaker D.J. et al. 2003. Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index. Irrigation science. Springer, 22(2), 95–104.
  • 25. Irmak S. 2010. Nebraska water and energy flux measurement, modeling, and research network (NEBFLUX)’, Transactions of the ASABE. American Society of Agricultural and Biological Engineers, 53(4), 1097–1115.
  • 26. Lima J.G.A. et al. 2021. Water requirement and crop coefficients of sorghum in Apodi Plateau. Revista Brasileira de Engenharia Agrícola e Ambiental. SciELO Brasil, 25, 684–688.
  • 27. Moorhead J.E. et al. 2019. Evaluation of evapotranspiration from eddy covariance using large weighing lysimeters’, Agronomy. Multidisciplinary Digital Publishing Institute, 9(2), 99.
  • 28. Naser M.A. et al. 2020. Using NDVI to differentiate wheat genotypes productivity under dryland and irrigated conditions. Remote Sensing. Multidisciplinary Digital Publishing Institute, 12(5), 824.
  • 29. Niu H., Wang D., Chen Y. 2020. Estimating crop coefficients using linear and deep stochastic configuration networks models and UAV-based Normalized Difference Vegetation Index (NDVI). In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 1485–1490.
  • 30. Rawat K.S., Singh S.K. 2016. Retrieval of Kc from SEBAL and comparison among NDVI and LAI based methods. Bulletin of Environmental and Scientific Research. Bulletin of Environmental and Scientific Research (BESR), 5(2).
  • 31. Rouse J., Schell J.A., Deering D.W.H.R.H. 1973. Monitoring vegetation systems in the great plains with ERTS. In: Proceeding of the “3rd ETRS Symposium”, NASA SP-351 1, US Government Printing Office, Washington, DC, USA, 309–317.
  • 32. Smith D.M., Allen S.J. 1996. Measurement of sap flow in plant stems’, Journal of Experimental Botany. Oxford University Press, 47(12), 1833–1844.
  • 33. Thapa S. et al. 2019. Use of NDVI for characterizing winter wheat response to water stress in a semi-arid environment’, Journal of Crop Improvement. Taylor & Francis, 33(5), 633–648.
  • 34. Trenberth K.E. et al. 2007. Estimates of the global water budget and its annual cycle using observational and model data. Journal of Hydrometeorology. American Meteorological Society, 8(4), 758–769.
  • 35. Yan H. et al. 2015. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecological modelling. Elsevier, 297, 42–59.
  • 36. Yimam Y.T., Ochsner T.E., Kakani V.G. 2015. Evapotranspiration partitioning and water use efficiency of switchgrass and biomass sorghum managed for biofuel. Agricultural Water Management. Elsevier, 155, 40–47.
  • 37. Zhang Y. et al. 2019. Maize crop coefficient estimated from UAV-measured multispectral vegetation indices. Sensors. Multidisciplinary Digital Publishing Institute, 19(23), 5250.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c8ffb0a4-154b-448f-b826-f7d144a594be
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