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DOI: 10.22059/jwim.2022.338119.961
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Comparison and study of the role of PYSEBAL and SEBS algorithms in estimating actual evapotranspiration in Qazvin plain
Mohadese Sadat Fakhar1, Abbas Kaviani2*
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.
Received: January 27, 2022 Accepted: May 26, 2022
Abstract
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.
Keywords: Crop Coefficient, Remote Sensing, Single source algorithm, Water management.
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MAE=0.35 MBE=-0.21 SE=0.17
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Figure 4. Estimated evapotranspiration time series using two algorithms SEBS and PYSEBAL by three sensors in Qazvin plain
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PYSEBAL SEBS
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Day of year Landsat-5
Lysimeter PYSEBAL SEBS
SEBS
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Figure 5. Actual evapotranspiration map estimated by three sensors in PYSEBAL and SEBS algorithms in August 2001.
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Table 2. Comparison of estimated plant coefficient results using SEBS and PYSEBAL algorithms by
MODIS sensor
SE Ia
R MBE MAE RMSE Algorithm
0.03 0.60 0.68 0.03 0.10 0.16
PYSEBAL
0.03 0.27 0.62 0.11 0.15 0.19 SEBS
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Figure 6. Investigation of values obtained from the plant coefficient by SEBS and PYSEBAL algorithms and its comparison with lysimetric data
0 0.3 0.6 0.9 1.2 1.5 1.8
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Day of year PYSEBAL
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1. Surface Energy Balance System
2. Surface Energy Balance Algorithm for Land 3. Mapping Evapotranspiration at high Resolution with
Internalized Calibration 4. Index of Agreement (Ia)
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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.
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(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, 271.
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.,
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