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Dynamic calibration for better SEBALI ET estimations: Validations and recommendations

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  • Mhawej, Mario
  • Elias, Georgie
  • Nasrallah, Ali
  • Faour, Ghaleb

Abstract

The widespread usage of the surface balance energy models coupled with remote sensing techniques proved their effectiveness in terms of accurately assessing evapotranspiration rates, crop water requirements as well as crop water usage/productivities. Still, such models require diverse inputs/elements, including climatic, topographic, soil, and remote sensing datasets. The availability of these inputs is questionable and outdated in many developing countries. Thus, an Improved Surface Energy Balance Algorithm for Land (SEBAL), also known as SEBALI, was proposed and adapted for regions lacking soil-related datasets. Still, the usage of a standard calibration method in the surface energy balance models, yielding constant value throughout the cropping season, requires further revision and improvement. In this context, this study proposed a novel dynamic calibration approach to be included within SEBALI, related to the actual wind speed and relative humidity conditions. It was followed by the calibrations and validations of monthly evapotranspiration SEBALI values. Datasets were retrieved randomly from four countries (i.e. Belgium, Germany, Italy and United States) representing different climatic zones, between 2013 and 2014, and based on Eddy Covariance flux towers’ outputs. When calibrated, results showed a Root Mean Square Error (RMSE) of 21.32 mm and an Average of Mean Error (AME) of 15.4 mm between monthly SEBALI outputs and flux towers’ datasets, with an R-squared value of 88.4%. When investigating SEBALI outputs on different buffered zones around the flux towers along a changing Normalized Difference Vegetation Index (NDVI), ET values were poorly correlated (i.e. R-squared lower than 60%) in any buffer zone outside the parcels’ boundary. RMSE showed values larger than 40 mm/month, even at a buffer zone of 250 m. This was related to the diverse Land Use Land Cover (LULC) classes, generating different evapotranspiration rates, found at the boundary of the selected parcels. With the proposed dynamic calibration, the enhanced SEBALI could be then implemented in any agricultural region missing soil-related datasets with high accuracy.

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  • 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).
    • Handle: RePEc:eee:agiwat:v:230:y:2020:i:c:s0378377419314209
    DOI: 10.1016/j.agwat.2019.105955
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    References listed on IDEAS

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    1. Yuei-An Liou & Sanjib Kumar Kar, 2014. "Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review," Energies, MDPI, vol. 7(5), pages 1-29, April.
    2. Allen, Richard G. & Pereira, Luis S. & Howell, Terry A. & Jensen, Marvin E., 2011. "Evapotranspiration information reporting: I. Factors governing measurement accuracy," Agricultural Water Management, Elsevier, vol. 98(6), pages 899-920, April.
    3. Zwart, Sander J. & Bastiaanssen, Wim G.M., 2007. "SEBAL for detecting spatial variation of water productivity and scope for improvement in eight irrigated wheat systems," Agricultural Water Management, Elsevier, vol. 89(3), pages 287-296, May.
    4. Mhawej, Mario & Caiserman, Arnaud & Nasrallah, Ali & Dawi, Ali & Bachour, Roula & Faour, Ghaleb, 2020. "Automated evapotranspiration retrieval model with missing soil-related datasets: The proposal of SEBALI," Agricultural Water Management, Elsevier, vol. 229(C).
    5. 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.
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    2. Allam, Mona & Mhawej, Mario & Meng, Qingyan & Faour, Ghaleb & Abunnasr, Yaser & Fadel, Ali & Xinli, Hu, 2021. "Monthly 10-m evapotranspiration rates retrieved by SEBALI with Sentinel-2 and MODIS LST data," Agricultural Water Management, Elsevier, vol. 243(C).
    3. Wei, Jun & Cui, Yuanlai & Zhou, Sihang & Luo, Yufeng, 2022. "Regional water-saving potential calculation method for paddy rice based on remote sensing," Agricultural Water Management, Elsevier, vol. 267(C).
    4. 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).
    5. 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).
    6. Ferreira, Thomás R. & Maguire, Mitchell S. & da Silva, Bernardo B. & Neale, Christopher M.U. & Serrão, Edivaldo A.O. & Ferreira, Jéssica D. & de Moura, Magna S.B. & dos Santos, Carlos A.C. & Silva, Ma, 2023. "Assessment of water demands for irrigation using energy balance and satellite data fusion models in cloud computing: A study in the Brazilian semiarid region," Agricultural Water Management, Elsevier, vol. 281(C).
    7. Teixeira, Antônio & Leivas, Janice & Struiving, Tiago & Reis, João & Simão, Fúlvio, 2021. "Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images," Agricultural Water Management, Elsevier, vol. 247(C).
    8. Mhawej, Mario & Nasrallah, Ali & Abunnasr, Yaser & Fadel, Ali & Faour, Ghaleb, 2021. "Better irrigation management using the satellite-based adjusted single crop coefficient (aKc) for over sixty crop types in California, USA," Agricultural Water Management, Elsevier, vol. 256(C).

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