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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).
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
325--335
Opis fizyczny
Bibliogr. 37 poz., rys.
Twórcy
autor
- College of Engineering, Al-Qasim Green University, 8, Al Qasim, Iraq
autor
- College of Engineering, Al-Qasim Green University, 8, Al Qasim, Iraq
autor
- College of Engineering, Al-Qasim Green University, 8, Al Qasim, Iraq
autor
- National Council for Scientific Research, Beirut, Lebanon
Bibliografia
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- 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
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- 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.
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- 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.
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- 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.
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- 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