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Estimating evapotranspiration of pistachio crop based on SEBAL algorithm using Landsat 8 satellite imagery

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  • Rahimzadegan, Majid
  • Janani, AdelehalSadat

Abstract

Stationary actual evapotranspiration (ET) estimation methods deal with some issues such as maintenance problems, the point data recording for a particular location, the complex hydrological process calculation, and requiring the expert workers. To solve this, ET estimation methods like Surface Energy Balance Algorithm for Land (SEBAL) method can be used based on the satellite images. However, the efficiency of the SEBAL model varies in ET estimation for different products and environmental conditions. On the other hand, pistachio is an important crop of Iran which little investigation were accomplished to estimate its ET using the presented models. Therefore, the purpose of this study is to investigate the efficiency of SEBAL algorithm in estimating ET of pistachio crop. In this regard, actual ET of a 100 hectares pistachio farm, located in Safaeieh region in the Semnan province, Iran, was selected as the study area. Implementations were performed using 29 Landsat 8 Operational Land Imager (OLI) images during the 2013–2017 period. The results were verified and compared with the results of evapotranspiration in standard condition estimated by the Intelligent Meteorological instrument (iMetos-Pessl) as the ground truth. The results of this study indicate the determination coefficient and Root Mean Square Error (RMSE) of the estimated actual ET for pistachio plant as 0.8 and 2.5 mm, respectively. Moreover, the results indicate the high spatial variability of ET in the pistachio growth period and the need to use a satellite data analysis method, like the SEBAL method, to prepare the ET map. Overall, the results show the good efficiency of the SEBAL model for estimating the actual ET of the pistachio product.

Suggested Citation

  • Rahimzadegan, Majid & Janani, AdelehalSadat, 2019. "Estimating evapotranspiration of pistachio crop based on SEBAL algorithm using Landsat 8 satellite imagery," Agricultural Water Management, Elsevier, vol. 217(C), pages 383-390.
    • Handle: RePEc:eee:agiwat:v:217:y:2019:i:c:p:383-390
    DOI: 10.1016/j.agwat.2019.03.018
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    References listed on IDEAS

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    1. Bastiaanssen, W. G. M. & Chandrapala, L., 2003. "Water balance variability across Sri Lanka for assessing agricultural and environmental water use," Agricultural Water Management, Elsevier, vol. 58(2), pages 171-192, February.
    2. Ramos, J.G. & Cratchley, C.R. & Kay, J.A. & Casterad, M.A. & Martnez-Cob, A. & Domnguez, R., 2009. "Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of N.E. Spain," Agricultural Water Management, Elsevier, vol. 96(4), pages 638-652, April.
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    Cited by:

    1. Zhang, Zhenyu & Li, Xiaoyu & Liu, Lijuan & Wang, Yugang & Li, Yan, 2020. "Influence of mulched drip irrigation on landscape scale evapotranspiration from farmland in an arid area," Agricultural Water Management, Elsevier, vol. 230(C).
    2. Lima, Carlos Eduardo Santos de & Costa, Valéria Sandra de Oliveira & Galvíncio, Josiclêda Domiciano & Silva, Richarde Marques da & Santos, Celso Augusto Guimarães, 2021. "Assessment of automated evapotranspiration estimates obtained using the GP-SEBAL algorithm for dry forest vegetation (Caatinga) and agricultural areas in the Brazilian semiarid region," Agricultural Water Management, Elsevier, vol. 250(C).
    3. Zheng Wang & Yue Huang & Tie Liu & Chanjuan Zan & Yunan Ling & Chenyu Guo, 2022. "Analysis of the Water Demand-Supply Gap and Scarcity Index in Lower Amu Darya River Basin, Central Asia," IJERPH, MDPI, vol. 19(2), pages 1-18, January.
    4. Wei, Jun & Cui, Yuanlai & Luo, Yufeng, 2023. "Rice growth period detection and paddy field evapotranspiration estimation based on an improved SEBAL model: Considering the applicable conditions of the advection equation," Agricultural Water Management, Elsevier, vol. 278(C).
    5. Sabzchi-Dehkharghani, Hamed & Nazemi, Amir Hossein & Sadraddini, Ali Ashraf & Majnooni-Heris, Abolfazl & Biswas, Asim, 2021. "Recognition of different yield potentials among rain-fed wheat fields before harvest using remote sensing," Agricultural Water Management, Elsevier, vol. 245(C).
    6. Santos, Jannaylton Everton Oliveira & Cunha, Fernando França da & Filgueiras, Roberto & Silva, Gustavo Henrique da & Castro Teixeira, Antônio Heriberto de & Santos Silva, Francisco Charles dos & Sediy, 2020. "Performance of SAFER evapotranspiration using missing meteorological data," Agricultural Water Management, Elsevier, vol. 233(C).
    7. Mhawej, Mario & Elias, Georgie & Nasrallah, Ali & Faour, Ghaleb, 2020. "Dynamic calibration for better SEBALI ET estimations: Validations and recommendations," Agricultural Water Management, Elsevier, vol. 230(C).

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