IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v243y2021ics0378377420309562.html
   My bibliography  Save this article

Monthly 10-m evapotranspiration rates retrieved by SEBALI with Sentinel-2 and MODIS LST data

Author

Listed:
  • Allam, Mona
  • Mhawej, Mario
  • Meng, Qingyan
  • Faour, Ghaleb
  • Abunnasr, Yaser
  • Fadel, Ali
  • Xinli, Hu

Abstract

Actual and potential evapotranspiration (ET) are important variables for regional and global environmental modelling. These elements provide better understanding and management of the hydrological cycle, particularly in relation to several environmental stresses affecting ecosystems. With the advancement of airborne techniques, remote sensing approaches have enabled an accurate estimation of crop actual ET with limited field visits. In this study, an enhanced version (i.e. m-SEBALI) of the improved surface energy balance model for land, surnamed SEBALI, will be presented. Main improvements concern the retrieval of 10-m ET values using Sentinel-2 images and MODIS Terra LST 1-km datasets. The other enhancement considers the usage of monthly-only climatic datasets, instead of the required hourly, daily and monthly data in SEBALI. Calibrations and validations were made in China, Belgium, Germany, Lebanon and Italy between 2013 and 2017, yielding adequate RSME (i.e. 20.06 mm/month) and AME (i.e. 15.69 mm/month) values. These countries represent four different climatic regions (i.e. continental semi-arid, Monsoon-influenced warm-summer humid continental, Mediterranean hot summer and Oceanic climates). Thus, Landast-8 thermal bands will be unnecessary to implement SEBALI. Also, the conversion between hourly and daily climatic data and monthly climatic datasets in SEBALI showed a Pearson value of 93.3 %. Thus, seven of the required inputs in SEBALI were eliminated in m-SEBALI, saving on time and resources. Furthermore, the wheat seasonal trend is produced in the Bekaa valley, Lebanon, between November 2018 and July 2019, showing an average ET value of 620 mm in a region with 510 mm of precipitations during the same period. Two peaks were visible in January and May signaling the different wheat phenological stages. The importance of m-SEBALI lies in providing an automated retrieval of 10-m ET, biomass production and water productivity (WP), among other variables, in diverse climatic regions. Such improvements shall improve the assessment of ET and WP towards better management of water resources, particularly in regions lacking some required inputs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:243:y:2021:i:c:s0378377420309562
    DOI: 10.1016/j.agwat.2020.106432
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377420309562
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2020.106432?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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).
    3. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jaouad El Hachimi & Abderrazak El Harti & Rachid Lhissou & Jamal-Eddine Ouzemou & Mohcine Chakouri & Amine Jellouli, 2022. "Combination of Sentinel-2 Satellite Images and Meteorological Data for Crop Water Requirements Estimation in Intensive Agriculture," Agriculture, MDPI, vol. 12(8), pages 1-17, August.
    2. 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).
    3. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. Al Zayed, Islam Sabry & Elagib, Nadir Ahmed & Ribbe, Lars & Heinrich, Jürgen, 2016. "Satellite-based evapotranspiration over Gezira Irrigation Scheme, Sudan: A comparative study," Agricultural Water Management, Elsevier, vol. 177(C), pages 66-76.
    8. Mohammed Magdy Hamed & Najeebullah Khan & Mohd Khairul Idlan Muhammad & Shamsuddin Shahid, 2022. "Ranking of Empirical Evapotranspiration Models in Different Climate Zones of Pakistan," Land, MDPI, vol. 11(12), pages 1-18, November.
    9. Taheri, Mercedeh & Emadzadeh, Maryam & Gholizadeh, Mohsen & Tajrishi, Masoud & Ahmadi, Mehdi & Moradi, Melika, 2019. "Investigating the temporal and spatial variations of water consumption in Urmia Lake River Basin considering the climate and anthropogenic effects on the agriculture in the basin," Agricultural Water Management, Elsevier, vol. 213(C), pages 782-791.
    10. Wagle, Pradeep & Gowda, Prasanna H. & Northup, Brian K., 2019. "Dynamics of evapotranspiration over a non-irrigated alfalfa field in the Southern Great Plains of the United States," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    11. Xue, Jingyuan & Fulton, Allan & Kisekka, Isaya, 2021. "Evaluating the role of remote sensing-based energy balance models in improving site-specific irrigation management for young walnut orchards," Agricultural Water Management, Elsevier, vol. 256(C).
    12. Hua Shi & George Xian & Roger Auch & Kevin Gallo & Qiang Zhou, 2021. "Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology," Land, MDPI, vol. 10(8), pages 1-30, August.
    13. Ramírez-Cuesta, J.M. & Intrigliolo, D.S. & Lorite, I.J. & Moreno, M.A. & Vanella, D. & Ballesteros, R. & Hernández-López, D. & Buesa, I., 2023. "Determining grapevine water use under different sustainable agronomic practices using METRIC-UAV surface energy balance model," Agricultural Water Management, Elsevier, vol. 281(C).
    14. Muhammad Arfan, 2022. "Mapping Impact of Farmer’s Organisation on the Equity of Water and Land Productivity: Evidence from Pakistan (Article)," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 61(2), pages 275-294.
    15. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    16. George P. Petropoulos & Prashant K. Srivastava & Maria Piles & Simon Pearson, 2018. "Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management," Sustainability, MDPI, vol. 10(1), pages 1-20, January.
    17. Kelechi Igwe & Vaishali Sharda & Trevor Hefley, 2023. "Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach," Land, MDPI, vol. 12(8), pages 1-26, July.
    18. Elnmer, Ayat & Khadr, Mosaad & Kanae, Shinjiro & Tawfik, Ahmed, 2019. "Mapping daily and seasonally evapotranspiration using remote sensing techniques over the Nile delta," Agricultural Water Management, Elsevier, vol. 213(C), pages 682-692.
    19. Walker, Elisabet & García, Gabriel A. & Venturini, Virginia & Carrasco, Aylen, 2019. "Regional evapotranspiration estimates using the relative soil moisture ratio derived from SMAP products," Agricultural Water Management, Elsevier, vol. 216(C), pages 254-263.
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:243:y:2021:i:c:s0378377420309562. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.