Document Type : *

Authors

1 PhD student of Climatology, Ahar Branch, Islamic Azad University, Ahar, Iran

2 Assistant Professor of Climatology, Department of Geography, Faculty of Humanities, Ahar Branch, Islamic Azad University, Ahar, Iran

3 Professor, Department of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran

Abstract

Estimation of Actual Evapotranspiration of Pasture Plants using SEBAL Algorithm (Case Study: Ahar County)

Abstract

The present study was an attempt to calculate the actual evapotranspiration rate of thyme, mint, and alfalfa pastures using the SEBAL method in the Ahar region, which is located south of Arsbaran forests and has numerous pastures of different species. For this purpose, 6 Landsat 8 satellite images were used between 2017 and 2020, which were in the initial and final periods of growth. The results were compared with the Penman-Monteith method. The results indicated that, based on the SEBAL method, the mint crop had the lowest rate of evapotranspiration in the initial period of crop growth on 2021/5/29, with a numerical value of 2.84 mm per day, and the alfalfa crop also in the initial crop growth period on 2019/6/11 has the highest rate of evapotranspiration with a numerical value of 3.49 mm per day. Additionally, in the final period of growth in the SEBAL method, the mint crop on 2018/8/28 had the lowest rate of evapotranspiration with a numerical value of 6.18 mm per day. Also, the thyme crop had the highest evapotranspiration rate in the final product growth period on 2022/7/19, with a numerical value of 7.41 mm per day. Finally, based on the comparisons made between the studied methods in the initial and final periods of growth in terms of squared mean error, mean absolute deviation, and coefficient of determination, it can be concluded that the SEBAL method compared to the Penman-Monteith method has an error rate with RMSE of 0.717, MAD of 0.658, and the coefficient of determination of 0.84 mm per day, which is acceptable.

Keywords: Evapotranspiration, SEBAL, Pasture, Water requirement, Ahar County

Keywords

Main Subjects

  1. اسدی، م.، باعقیده، م.، ولیزاده کامران، خ.، و ادب، ح. (1399). ارزیابی همبستگی پوشش گیاهی با دمای سطح زمین با استفاده از تصاویر ماهواره‌ای (مطالعه موردی: استان اردبیل). پژوهش‌های محیط‌زیست، 11(22)، 98-87.
  2. اسدی، م.، ولیزاده کامران، خ.، باعقیده، م.، و ادب، ح. (1399). برآورد میزان تبخیر و تعرق واقعی با استفاده از الگوریتم سبال کوهستانی بر پایه گیاه نخود (مطالعه موردی: نیمه شمالی استان اردبیل). هیدروژئومورفولوژی، 7(22)، 85-67.
  3. اسدی، م.، ولیزاده کامران، خ.، باعقیده، م.، و ادب، ح. (1399). مقایسه و تخمین سپیدایی سطوح مختلف کاربری اراضی با استفاده از روش سبال و متریک. نشریه تحقیقات کابردی علوم جغرافیایی، ۲۰(۵۹)، 171-157.
  4. سهیلی­فر، ز.، میرلطیفی، س. م.، ناصری، عب. ع.، و عصاری، م. (1392). برآورد تبخیر و تعرق واقعی نیشکر با استفاده از داده­های سنجش از دور در اراضی کشت و صنعت نیشکر میرزا کوچک خان. نشریه دانش آب و خاک، 23(1)، 163-151.
  5. ولیزاده کامران، خ.، و اسدی، م. (1402). برآورد سطح زیرکشت گندم با استفاده از تصاویر ماهواره لندست ۸ (مطالعه موردی: نیمه شمالی استان اردبیل). فضای جغرافیایی، ۲۳(۸۱)، 59-45.

 

  1. Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133(4), 380-394.‏
  2. Allen, R., Waters, R., Tasumi, M., Trezza, R., & Bastianssen, W. (2002(. SEBAL (surface energy balance algorithms for land)- Advanced training and user's manual, version 1.0.
  3. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (Eds.). (1998). FAO Irrigation and Drainage. No. Paper 56.
  4. Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for Mapping Evapotranspiration with Internalized Calibration (METRIC) model. Journal of Irrigation Drainage Engineering, 133, 380–394.
  5. Al Zayed, I. S., Elagib, N. A., Ribbe, L., & Heinrich, J. (2016). Satellite-based evapotranspiration over Gezira Irrigation Scheme, Sudan: A comparative study. Agricultural Water Management, 177, 66-76.

 

  1. Asadi, M., & Karami, M. (2020). Estimation of evapotranspiration in Fars province using experimental indicators. Journal of Applied Researches in Geographical Sciences, 20(56), 159-175.
  2. Asadi, M., & Kamran, K. V. (2022). Comparison of SEBAL, METRIC, and ALARM algorithms for estimating actual evapotranspiration of wheat crop. Theoretical and Applied Climatology, 149(1-2), 327-337.
  3. Asadi, M., & Kamran, K. V. (2023). Estimating selected cultivated crop water requirement-based surface energy balance algorithm. Arabian Journal of Geosciences, 16, 298.
  4. Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). Journal of Hydrology, (212–213), 198–212.
  5. Costa, J. D. O., Coelho, R. D., Wolff, W., José, J. V., Folegatti, M. V., & Ferraz, S. F. D. B. (2019). Spatial variability of coffee plant water consumption based on the SEBAL algorithm. Scientia Agricola, 76(2), 93-101.
  6. de Arellano, J. V.-G., Van Heerwaarden, C. C., Van Stratum, B. J. H., & Van Den Dries, K. (2015). Atmospheric boundary layer: Integrating air chemistry and land interactions. New York: Cambridge University Press.
  7. Du, J., Song, K., Wang, Z., Zhang, B., & Liu, D. (2013). Evapotranspiration estimation based on MODIS products and surface energy balance algorithms for land (SEBAL) model in Sanjiang Plain, Northeast China. Chinese Geographical Science, 23(1), 73-91.
  8. Elnmer, A., Khadr, M., Kanae, S., & Tawfik, A. (2019). Mapping daily and seasonally evapotranspiration using remote sensing techniques over the Nile delta. Agricultural Water Management, 213, 682-692.
  9. Genanu, M., Alamirew, T., Senay, G., & Gebremichael, M. (2017). Remote sensing based estimation of evapo-transpiration using selected algorithms: The case of Wonji Shoa Sugar Cane Estate, Ethiopia. Preprints, 2016080098.
  10. Hu, Z., Yu, G., Zhou, Y., Sun, X., Li, Y., Shi, P., …, & Li, S., (2009). Partitioning of evapotranspiration and its controls in four grassland ecosystems: Application of a two-source model. Agricultural and Forest Meteorology, 149, 1410–1420.
  11. Karami, M., & Asadi, M. (2016). Estimates and zoning of reference evapotranspiration by FAO-penman-monteith (Case study: North West of Iran). International Journal of Scientific Research in Science, Engineering and Technology, 2(1), 210-216.
  12. Kosa, P. (2011). The effect of temperature on actual evapotranspiration based on Landsat 5 TM satellite imagery. In L. Labedzki (Ed.), Evapotranspiration. DOI: 10.5772/14012/
  13. Kundu, S., Mondal, A., Khare, D., Hain, C., & Lakshmi, V. (2018). Projecting climate and land use change impacts on actual evapotranspiration for the Narmada River Basin in central India in the future. Remote Sensing, 10(4), 578.
  14. Lage, M., Bamouh, A., Karrou, M., & El Mourid, M. (2003). Estimation of rice evapotranspiration using a microlysimeter technique and comparison with FAO Penman-Monteith and Pan evaporation methods under Moroccan conditions. Agronomie, 23, 625–631.
  15. Laipelt, L., Henrique Bloedow Kayser, R., Santos Fleischmann, A., Ruhoff, A., Bastiaanssen, W., Erickson, T. A., & Melton, F. (2021). Long-term monitoring of evapotranspiration using the SEBAL algorithm and google earth engine cloud computing. Remote Sensing, 178, 81–96.
  16. Liu, X., Xu, J., Wang, W., Lv, Y., & Li, Y. (2020). Modeling rice evapotranspiration under water-saving irrigation condition: Improved canopy-resistance-based. Journal of Hydrology, 590, 125435
  17. Ma, W., Hafeez, M., Rabbani, U., Ishikawa, H., & Ma, Y. (2012). Retrieved actual ET using SEBS model from Landsat-5 TM data for irrigation area of Australia. Atmospheric Environment, 59, 408-414.
  18. Mahmoud, S. H., & Alazba, A. A. (2016). A coupled remote sensing and the Surface Energy Balance based algorithms to estimate actual evapotranspiration over the western and southern regions of Saudi Arabia. Journal of Asian Earth Sciences, 124, 269-283.
  19. Mather, P., & Tso, B. (2016). Classification methods for remotely sensed data. Florida: CRC Press.
  20. McShane, R. R., Driscoll, K. P., & Sando, R. (2017). A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents. Scientific Investigations Report, Series Number 2017-5087.
  21. Mkhwanazi, M., Chávez, J. L., & Andales, A. A. (2015). SEBAL-A: A remote sensing ET algorithm that accounts for advection with limited data. Part I: Development and validation. Remote Sensing, 7(11), 15046-15067.
  22. Oberg, J. W., & Melesss, A. M. (2006). Evapotranspiration dynamics at an ecohydrological restoration site: an energy balance and remote sensing approach. JAWRA Journal of the American Water Resources Association, 42(3), 565-582.
  23. Omidvar, H., Song, J., Yang, J., Arwatz, G., Wang, Z.H., Hultmark, M., Kaloush, K., & Bou‐Zeid, E. (2018). Rapid modification of urban land surface temperature during rainfall. Water Resources Research, 54(7), 4245-4264.
  24. Owaneh, O. M., & Suleiman, A. A. (2018). Comparison of the performance of ALARM and SEBAL in estimating the actual daily ET from Satellite data. Journal of Irrigation and Drainage Engineering, 144(9), 04018024.
  25. Qiu, R., Liu, C., Cui, N., Wu, Y., Wang, Z., & Li, G. (2019). Evapotranspiration estimation using a modified Priestley-Taylor model in a rice-wheat rotation system. Agricultural Water Management, 224, 105755.
  26. Rawat, K. S., Singh, S. K., Bala, A., & Szabó, S. (2019). Estimation of crop evapotranspiration through spatial distributed crop coefficient in a semi-arid environment. Agricultural Water Management, 213, 922-933.
  27. Ruhoff, A., Paz, A. R., Collischonn, W., Aragao, L. E. O. C., Rocha, H. R., & Malhi, Y. S. (2012). A MODIS-based energy balance to estimate evapotranspiration for clear-sky days Brazilian tropical savannas. Remote Sensing Journal, 4, 703-725.
  28. Silva, B. B. D., Mercante, E., Boas, M. A. V., Wrublack, S. C., & Oldoni, L. V., (2018). Satellite-based ET estimation using Landsat 8 images and SEBAL model. Revista Ciência Agronômica, 49(2), 221-227.‏
  29. Singh, R. K., Irmak, A., Irmak, S., & Martin, D. L. (2008). Application of SEBAL model for mapping evapotranspiration and estimating surface energy fluxes in south-central Nebraska. Journal of irrigation and Drainage Engineering, 134(3), 273-285.
  30. Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1), 85-100.
  31. Valayamkunnath, P., Sridhar, V., Zhao, W., & Allen, R. G. (2018). Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, largeaperture scintillometer, and modeling across three ecosystems in a semiarid climate. Agricultural and Forest Meteorology, 248, 22–47.
  32. Yang, Y., Zhou, X., Yang, Y., Bi, S., Yang, X., & Li Liu, D., (2018). Evaluating water-saving efficiency of plastic mulching in Northwest China using remote sensing and SEBAL. Agricultural Water Management, 209, 240-248.‏
  33. Zhou, X., Bi, S., Yang, Y., Tian, F., & Ren, D. (2014). Comparison of ET estimations by the three-temperature model, SEBAL model and eddy covariance observations. Journal of Hydrology, 519, 769-776.‏
  34. Zotarelli, L., Dukes, M. D., Romero, C. C., Migliaccio, K. W., & Morgan, K. T. (2010). Step by step calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method). Institute of Food and Agricultural Sciences. University of Florida
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