Comparison of SEBS and Penman-Monteith methods in estimating water requirement of corn crop (Case study: Meshginshahr)

Document Type : Original Article

Authors

1 University of Tabriz

2 Hakim Sabzevari University

10.22034/rsgi.2022.14394

Abstract

Determining the water requirement of agricultural products is one of the most important ways to achieve water management efficiency. Therefore, the main purpose of this study is to estimate the water requirement of corn in Meshginshahr, Ardabil province. For this purpose, 10 cloudless images of Landsat 8 satellite from 2004 to 2016 and the SEBS method were used. The results were compared with Penman-Monteith and evaporation pan methods. The results showed that in the SEBS method the water requirement of corn crop on 2010.8.3was equal to 8.14 mm/day and on 2015.11.30 equal to 1.09 mm/day and in Penman-Monteith method on 2013.7.19 equal to 9.94 mm/day and on 2015.11.30 equal to 3.12 mm/day, which has the highest and lowest actual evaporation and transpiration, respectively. SEBS method has the highest error rate with RMSE equal to 1.466 and MAD equal to 1.325 mm/day compared to evaporation pan. SEBS method has the lowest error rate with RMSE equal to 1.199 and MAD equal to 0.947 mm/day compared to the Penman-Monteith method. Also, by examining the values of the coefficient of determination, it was found that the SEBS method has the highest coefficient of determination (0.8677) with the Penman-Monteith method and then with the evaporation pan method (0.8247).

Keywords

تعیین نیاز آبی محصولات کشاورزی یکی از مهمترین راههای رسیدن به بهره وری مدیریت آب میباشد. لذا هدف اصلی این پژوهش برآورد نیاز آبی محصول ذرت در مشگینشهر استان اردبیل میباشد و برای این هدف از 02 تصویر بدون ابر ماهواره لندست 0 از سال 0220 تا 0202 و روش SEBS استفاده و نتایج حاصل از آن با روشهای پنمن مانتیث و تشت تبخیر مقایسه گردید. نتایج بیانگر آن بود که در روش SEBS نیاز آبی محصول ذرت در تاریخ 3/7/0202 برابر 00/0  میلی متر در روز و در تاریخ 32/00/0202 برابر 20/0 میلیمتر در روز و در روش پنمن مانتیث در تاریخ 00/7/0203 برابر 00/0 میلیمتر در روز و در تاریخ 32/00/0202 برابر 00/3 میلی متر در روز که به ترتیب بیشترین و کمترین میزان تبخیر و تعرق واقعی را دارا میباشد. روش SEBS نیز در مقایسه با تشت تبخیر بیشترین میزان خطا با RMSE برابر با 022/0 و MAD برابر با 302/0 و در مقایسه با روش پنمن مانتیث کمترین میزان خطا را با RMSE برابر با 000/0 و MAD برابر با 007/2 دارا است. همچنین با بررسی مقادیر ضریب تعیین مشخص گردید که روش SEBS بیشترین ضریب تعیین) 0277/2( را با روش پنمن مانتیث و بعدازآن با روش تشت تبخیر )0007/2( دارا است.

واژگان کلیدی: مشگین شهر، نیاز آبی ،SEBS، پنمن مانتیث، اردبیل.

1-Allen, RG., Tasumi, M., Morse A., Trezza R. (2005). A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrigation and Drainage systems, 19(3-4): 251-268.
2-Atasever, UH., Ozkan, C. (2018). A New SEBAL Approach Modified with Backtracking Search Algorithm for Actual Evapotranspiration Mapping and On-Site Application. Journal of the Indian Society of Remote Sensing, 46(8): 1213-1222.
3-Bhattarai, N., Shaw, SB., Quackenbush, LJ,. Im, J., Niraula, R. (2016). Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate. International Journal of Applied Earth Observation and Geoinformation, 49: 75-86.
4-Bouwer, L., Biggs, T., Aerts, C. (2008). Estimates of spatial variation in evaporation using satellite-derived surface temperature and a water balance model. Hydrological Processes, 22(5): 670–682. 
5-Budyko, M. (1971). Climate and Life. Orlando: Academic Press: 1–.7
6-Costa, JDO., Coelho, RD., Wolff, W., José, JV., Folegatti, MV., Ferraz, SFDB. (2019). Spatial variability of coffee plant water consumption based on the SEBAL algorithm. Scientia Agricola, 76(2): 93-101.
7-Elhag, M., Psilovikos, A., Manakos, I., Perakis, K. (2011). Application of the SEBS water balance model in estimating daily evapotranspiration and evaporative fraction from remote sensing data over the Nile Delta. Water Resources Management, 25(11): 2731-2742.
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-Flannigan, M., Stocks, B., Turetsky, M. (2009). Impacts of climate change on fire activity and fire management in the circumboreal forest. Global Change Biology, 15(3): 549–560.
10-Grosso, C., Manoli, G., Martello, M., Chemin, Y., Pons, D., Teatini, P., Morari, F. (2018). Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations. Remote Sensing, 10(9): 1452.
11-Häusler, M., Nunes, JP., Soares, P., Sánchez, JM., Silva, JM. Warneke, T., Pereira, JM. (2018). Assessment of the indirect impact of wildfire (severity) on actual evapotranspiration in eucalyptus forest based on the surface energy balance estimated from remote-sensing techniques. International Journal of Remote Sensing: 1-.62
12-Karami, M., Asadi M. (2016). Estimates and Zoning of reference evapotranspiration by FAOPenman-Monteith (Case Study: North West of Iran). IJSRSET, 2(1): 210-216.
 
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- Kustas W. Norman J. (1996). Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrological Sciences Journal (United Kingdom), 41(4): 495–516. 
15-Liaqat, UW., 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.
16-Losgedaragh, SZ., Rahimzadegan, M. (2018). Evaluation of SEBS, SEBAL, and METRIC models in estimation of the evaporation from the freshwater lakes (Case study: Amirkabir dam, Iran). Journal of Hydrology, 561: 523-531.
17-Lu, J., Li, ZL., Tang, R., Tang, BH., Wu, H., Yang, F., Zhou, G. (2013). Evaluating the SEBSestimated evaporative fraction from MODIS data for a complex underlying surface. Hydrological Processes, 27(22): 3139-3149.
18-Ma, W., Hafeez, M., Ishikawa, H., Ma, Y. (2013). Evaluation of SEBS for estimation of actual evapotranspiration using ASTER satellite data for irrigation areas of Australia. Theoretical and applied climatology, 112(3-4): 609-616.
19-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.
20-Maayar, M., Chen, J. (2006). Spatial scaling of evapotranspiration as affected by heterogeneities in vegetation, topography, and soil texture. Remote Sensing of Environment, 102(1–2): 33–51.
21-Miller, G., Baldocchi, D., Law, B. (2007). An analysis of soil moisture dynamics using multiyear data from a network of micrometeorological observation sites. Advances in Water Resources, 30(5):1065–.1801
22-Mkhwanazi, M., Chávez, JL., Andales, AA. (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-.76051
23-Rauwerda, J., Roerink, GJ., Su, Z. (2002). Estimation of evaporative fractions by the use of vegetation and soil component temperature determined by means of dual-looking remote sensing (No. 580). Alterra: 149 Page.
24-Rawat, KS., Singh, SK., Bala, A., Szabó, S. (2019). Estimation of crop evapotranspiration through spatial distributed crop coefficient in a semi-arid environment. Agricultural Water Management, 213: 922، صص 02-0
Application of Remote Sensing and GIS in Environmental Sciences, Vol. 1, No. 1, Winter 2022, pp. 1-16
25-Ruhoff, AL., Paz, AR., Collischonn, W., Aragao, LE., Rocha, HR., Malhi, YS., (2012). A MODIS-based energy balance to estimate evapotranspiration for clear-sky days in Brazilian tropical savannas. Remote Sensing, 4(3): 703-725.
26-Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and earth system sciences, 6(1): 85-100.
27-Sun, Z., Wei, B., Su, W., Shen, W., Wang, C., You, D., Liu, Z. (2011). Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China. Mathematical and Computer Modelling, 54(3-4): 1086-1092.
28-Timmermans, J., Su, Z., Tol, C., Verhoef, A., Verhoef, W. (2013). Quantifying the uncertainty in estimates of surface–atmosphere fluxes through joint evaluation of the SEBS and SCOPE models. Hydrology and earth system sciences, 17(4): 1561-1573.
29-Zare, M., Pakparvar, M., Jamshidi, S., Bazrafshan, O., Ghahari, G. (2021). Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model. Water Resources Management: 1-.61
30-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-.677
  • Receive Date: 24 October 2021
  • Revise Date: 26 December 2021
  • Accept Date: 07 February 2022