مقایسه و بررسی نقش الگوریتم‌های PYSEBAL و SEBS در برآورد تبخیر-تعرق واقعی در دشت قزوین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین‌المللی امام خمینی (ره)، قزوین، ایران.

2 دانشیار، گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران.

چکیده

روش‌های مختلفی به‌منظور پایش تبخیر-تعرق وجود دارد که به‌طورعمده به‌صورت نقطه­ای اندازه­گیری می‌کنند که تعمیم آن برای کل منطقه دشوار خواهد بود. سال­های اخیر روش‌های مبتنی بر سنجش‌ازدور به‌منظور برآورد تبخیر- تعرق واقعی در محدوده وسیع­ موردتوجه قرارگرفته است. در این پژوهش مقدار تبخیر-تعرق واقعی به­صورت روزانه در دشت قزوین توسط دو الگوریتم SEBS و PYSEBAL برای 15 تصویر TM، 22 تصویر  ETM+ و 24 تصویر MODIS بدون ابر و برف در طی سال­های 2000 تا 2003 میلادی استفاده شد. نتایج حاصل با داده‌های یک لایسیمتر زهکش­دار کشت­شده با چمن در محدوده دشت قزوین مورد مقایسه قرار گرفتند. از مقایسه خروجی­های به‌دست‌آمده از دو الگوریتم این نتیجه به‌دست آمد که الگوریتم PYSEBAL با بهره­گیری از جدید­ترین روش‌های برآورد تبخیر- تعرق هم‌چون عدم انتخاب دو پیکسل گرم و سرد توسط کاربر و استفاده حداقلی از داده­های زمینی توانسته است بسیاری از ضعف‌های سایر الگوریتم­ها را پوشش دهد. به‌طوری‌که الگوریتم PYSEBAL در هر سه سنجنده MODIS، LANDSAT-ETM+ و LANDSAT-TM به‌ترتیب با مقدار RMSE ، برابر با 45/0، 46/0 و 02/2 میلی­متر بر روز، و مقدار R2  96/0، 95/0 و 82/0 عملکرد بهتری را نسبت به الگوریتم SEBS در محدوده موردمطالعه داشته است. در ادامه با توجه به این‌که تعیین مقدار آبی که صرف تبخیر- تعرق می‌شود از اساسی­ترین عوامل در برنامه­ریزی به­منظور رسیدن به محصول بیش‌تر است، ضریب Kc می‌تواند راهنمای مناسب و سریعی در مدیریت آبیاری محسوب شود. بررسی­های انجام‌شده بر روی گیاه چمن نشان از دقت بالای مدل PYSEBAL با مقدار ضریب همبستگی 68/0 در برآورد این ضریب داشته و در نهایت استفاده از روش‌های مبتنی بر سنجش‌ازدور می­تواند به‌عنوان جایگزینی مناسب به‌منظور جلوگیری از صرف هزینه زیاد و بهبود مدیریت آب در منطقه باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison and study of the role of PYSEBAL and SEBS algorithms in estimating actual evapotranspiration in Qazvin plain

نویسندگان [English]

  • Mohadese Sadat Fakhar 1
  • Abbas Kaviani 2
1 M. Sc. Student, Department of Water Science and Engineering, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran.
2 Associate Professor, Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran.
چکیده [English]

There are several methods for monitoring evapotranspiration, which are mainly measured on a point that will be difficult to generalize to the whole area. In recent years, remote sensing-based methods for estimating actual evapotranspiration have been widely considered. In this study, the amount of actual evapotranspiration per day in Qazvin plain was used by SEBS and PYSEBAL algorithms for 15 TM images, 22 ETM + images and 24 MODIS images without clouds and snow during the years 2000 to 2003. The results were compared with a drained lysimeter planted with grass in the Qazvin plain. Comparing the outputs obtained from the two algorithms, it was concluded that the PYSEBAL algorithm, using the latest methods of estimating evapotranspiration, such as the user not selecting two hot and cold pixels and minimal use of ground data, has been able to cover many of the weaknesses of other algorithms, so that the PYSEBAL algorithm in all three MODIS sensors, LANDSAT-ETM+ and LANDSAT-TM respectively with RMSE values (0.45, 0.46 and 2.02 mm/day, and R2 values of 0.96, 0.95 and 0.82) had better performance than SEBS algorithm in the study area. Furthermore, considering that determining the amount of water used for evapotranspiration is one of the most basic factors in planning in order to achieve more product, Kc coefficient can be considered as a suitable and fast guide in irrigation management. Studies on grass plant show the high accuracy of PYSEBAL model in estimating this coefficient. And finally, the use of remote sensing methods can be a good alternative to avoid high costs and improve water management in the region.

کلیدواژه‌ها [English]

  • Crop Coefficient
  • Remote Sensing
  • Single source algorithm
  • Water management
  1. Bansouleh, B. F., Karimi, A. R., & Hesadi, H. (2015). Evaluation of SEBAL and SEBS algorithms in the estimation of maize evapotranspiration. International Journal of Plant & Soil Science, 350-358.
  2. Bastiaanssen, Wilhelmus Gerardus Maria. (1995). Regionalization of surface flux densities and moisture indicators in composite terrain: A remote sensing approach under clear skies in Mediterranean climates. Wageningen University and Research,
  3. Bastiaanssen, Wim G M. (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229(1-2), 87-100.
  4. Caiserman, A., Amiraslani, F., & Dumas, D. (2021). Assessment of the agricultural water budget in southern Iran using Sentinel-2 to Landsat-8 datasets. Journal of Arid Environments, 188, 104461.
  5. Daughtry, C. S. T., Biehl, L. L., & Ranson, K. J. (1989). A new technique to measure the spectral properties of conifer needles. Remote Sensing of Environment, 27(1), 81-91.
  6. Ebrahimi pak N.A. (2000). Determination of evapotranspiration potential of reference plant (grass) by lysymeter method and comparison with experimental methods in Qazvin. Ministry of Agricultural Jihad, Agricultural Research, Education and Promotion Organization, Qazvin Agricultural and Natural Resources Research Center. (In Persian).
  7. Gowda, P. H., Chavez, J. L., Colaizzi, P. D., Evett, S. R., Howell, T. A., & Tolk, J. A. (2008). ET mapping for agricultural water management: present status and challenges. Irrigation Science, 26(3), 223-237.
  8. Hailegiorgis, W. S. (2006). Remote Sensing analysis of summer time Evapotranspiration using SEBS algorithm. ITC, Enschede, 130.
  9. Hessels, T., van Opstal, J., Trambauer, P., Bastiaanssen, W., Faouzi, M., Mohamed, Y., & ErRaji, A. (2017). pySEBAL Version 3.3. 7.
  10. Jafari Sayadi, F., Gholami Sefidkouhi, M. A., & Ziyaeetabar Ahmadi, M. (2018). Leaf Area Index and Crop Coefficient Estimation from Operational Land Imager (OLI) Sensor Data. Journal of Water Research in Agriculture, 32(3), 395-404. (In Persian) 
  11. Jalili, J., Radmanesh, F., Naseri, A. A., Ali, A., & Zarei, H. A. (2020). Estimation of sugar cane Evapotranspiration using SEBAL and SEBS algorithms and priestly-taylor method (Case study of amir kabir cultivation and industry). JWSS-Isfahan University of Technology, 24(3), 17-32.
  12. Kalma, J. D., McVicar, T. R., & McCabe, M. F. (2008). Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4), 421-469.
  13. Kazamias, A. P., & Sapountzis, M. (2017). Spatial and temporal assessment of potential soil erosion over Greece. Water, 59, 315-321.
  14. Ke, Y., Im, J., Park, S., & Gong, H. (2016). Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sensing, 8(3), 215.
  15. Kunstmann, H., Oluwadare, A., Okogbue, E., Akinluyi, F., Arnault, J., Tayari, S., Hingerl, L., & Bliefernicht, J. (2018). Comparison of sebal estimated heat fluxes and evapotranspiration using field and remote sensing data in the Sudanian Savanna in West Africa. International Journal of Agriculture and Environmental Research, 4(2), 352-374.
  16. Liaqat, U. W., & Choi, M. (2015). Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using Landsat satellite images. Agricultural and Forest Meteorology, 214, 60-79.
  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. Mahour, M., Stein, A., Sharifi, A., & Tolpekin, V. (2015). Integrating super resolution mapping and SEBS modeling for evapotranspiration mapping at the field scale. Precision Agriculture, 16(5), 571-586.
  19. McMahon, T. A., Finlayson, B. L., & Peel, M. C. (2016). Historical developments of models for estimating evaporation using standard meteorological data. Wiley Interdisciplinary Reviews: Water, 3(6), 788-818.
  20. Mutiga, J. K., Su, Z., & Woldai, T. (2010). Using satellite remote sensing to assess evapotranspiration: case study of the upper Ewaso Ng’iro North Basin, Kenya. International Journal of Applied Earth Observation and Geoinformation, 12, S100-S108.
  21. Nazari, Bijan. Fakhar, M. S. (2021). Planning and management of greenhouse cultivation; with focusing on water management and productivity. Imam Khomeini international university publications. (In Persian) 
  22. Nishida, K., Nemani, R. R., Running, S. W., & Glassy, J. M. (2003). An operational remote sensing algorithm of land surface evaporation. Journal of Geophysical Research: Atmospheres, 108(D9).
  23. Owlia, A. H., & Sima, S. (2021). Uncertainties in estimation of basin-scale actual evapotranspiration using SEBAL. Iranian Journal of Soil and Water Research, 52(5), 1209-1221. (In Persian) 
  24. Ramírez-Cuesta, J. M., Allen, R. G., Zarco-Tejada, P. J., Kilic, A., Santos, C., & Lorite, I. J. (2019). Impact of the spatial resolution on the energy balance components on an open-canopy olive orchard. International Journal of Applied Earth Observation and Geoinformation, 74, 88-102.
  25. Sánchez, J. M., Kustas, W. P., Caselles, V., & Anderson, M. C. (2008). Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sensing of Environment, 112(3), 1130-1143.
  26. Sawadogo, A., Gundogdu, K. S., Traoré, F., Kouadio, L., & Hessels, T. (2020). estimating in season actual evapotranspiration over a large-scale irrigation scheme in resurcelimited conditions. Comptes Rendus de l’Académie Bulgare Des Sciences, 73(10).
  27. Sawadogo, A., Hessels, T. İ. M., Gündoğdu, K. S., Demir, A. O., Mustafa, Ü., & Zwart, S. J. (2020). Comparative analysis of the PYSEBAL model and lysimeter for estimating actual evapotranspiration of soyabean crop in Adana, Turkey. International Journal of Engineering and Geosciences, 5(2), 60-65.
  28. Senay, G. B., Friedrichs, M., Singh, R. K., & Velpuri, N. M. (2016). Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin. Remote Sensing of Environment, 185, 171-185.
  29. Shan, X., van de Velde, R., Wen, J., He, Y., & Su, Z. (2007). Regional evapotranspiration over the Arid Inland Heihe River Basin in Northwest China, ESA’s Publications Division as Special Publication SP-655. Proceedings of the Dragon Programme Final Results.
  30. Singh, R. K., & Senay, G. B. (2016). Comparison of four different energy balance models for estimating evapotranspiration in the Midwestern United States. Water, 8(1), 9.
  31. Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1), 85-100.
  32. Tang, R., Li, Z.-L., & Tang, B. (2010). An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation. Remote Sensing of Environment, 114(3), 540-551.
  33. Wu, C., Cheng, C., Lo, H., & Chen, Y. (2010). Study on estimating the evapotranspiration cover coefficient for stream flow simulation through remote sensing techniques. International Journal of Applied Earth Observation and Geoinformation, 12(4), 225-232.