توسعه الگوریتم SM-SEBAL به منظور محاسبه تبخیر و تعرق واقعی به کمک سنجش از دور

فصلنامه علمی مهندسی منابع آب

مقاله 10، دوره 11، شماره 38، آذر 1397، صفحه 107-128   

نوع مقاله: مقاله پژوهشی

نویسندگان

فرهاد میرزایی^{* 1}؛ محمدرضا کشاورز²؛ مجید وظیفه دوست³

¹دانشیار و عضو هیئت علمی آبیاری و آبادانی دانشکده کشاورزی و منابع طبیعی دانشگاه تهران

²فارغ التحصیل دکتری آبیاری و آبادانی دانشکده کشاورزی و منابع طبیعی دانشگاه تهران

³عضو هیئت علمی مهندسی آب دانشکده کشاورزی دانشگاه گیلان

تاریخ دریافت: 19 خرداد 1395،  تاریخ بازنگری: 01 اسفند 1395،  تاریخ پذیرش: 02 بهمن 1397 

چکیده

روش Sufrace Energy BALance (SEBAL)یکی از پر‌کاربردترین روش‌های تعیین تبخیر و تعرق واقعی به کمک سنجش از دور است. با این حال فرض خطی بودن رابطه اختلاف دما و دمای سطح زمین و نیز استفاده از دو پیکسل گرم و سرد که توسط کاربر شناسایی می‌شوند از نقاط ضعف این روش محسوب می‌شود. روش Modified SEBAL (M-SEBAL) علاوه بر اصلاح این مشکل، دو مفهوم جدید لبه سرد و گرم را جایگزین پیکسل‌های سرد و گرم می‌کند که تاثیر بسزایی در افزایش دقت و نیز خودکارسازی اجرای الگوریتم و عدم نیاز به کاربر ماهر دارد. با این حال تعیین لبه گرم نیاز به محاسبات زیاد و در نتیجه بالا رفتن زمان محاسبه و امکان خطای محاسباتی دارد. در این تحقیق، شکلی ساده شده از روش M-SEBAL با نام Simplified M-SEBAL (SM-SEBAL) توسعه یافته است و نتایج حاصل از اجرای سه روش بر روی بیش از 300 لایه تصویری MODIS با داده‌های لایسیمتری و نیز داده‌های زمینی بیلان انرژی مزرعه تحقیقاتی دانشگاه تهران واقع در کرج مقایسه شده است. نتایج حاصل از اجرای سه الگوریتم نشان می‌دهد که الگوریتم SEBAL تبخیر و تعرق را بیش برآورد و دو الگوریتم دیگر آنرا اندکی کم برآورد می‌کنند. بیشترین خطای محاسبه شار گرمای محسوس مربوط به روش M-SEBAL با 87/33 درصد و کمترین میزان خطا مربوط به نتایج روش SEBAL (23/11 درصد) می‌باشد. با این حال حداکثر خطا در مقادیر تبخیر تعرق روزانه در نتایج روش SEBAL (56/3 درصد) و کمترین میزان خطا در روش SM-SEBAL ( 18/1 درصد) مشاهده شد.

کلیدواژه‌ها

سنجش از دور؛ تبخیر و تعرق؛ سبال؛ M-SEBAL؛ SM-SEBAL

عنوان مقاله [English]

Development of a new algorithm (SM-SEBAL) to evaluate Evapotranspiration based on remote sensing data

نویسندگان [English]

Farhad Mirzaee¹؛ Mohammadreza Keshavarz²؛ Majid Vazifedoust³

¹دانشیار و عضو هیئت علمی آبیاری و آبادانی دانشکده کشاورزی و منابع طبیعی دانشگاه تهران

²فارغ التحصیل دکتری آبیاری و آبادانی دانشکده کشاورزی و منابع طبیعی دانشگاه تهران

³عضو هیئت علمی مهندسی آب دانشکده کشاورزی دانشگاه گیلان

چکیده [English]

The algorithm of Sufrace Energy BALance (SEBAL) is one of the most widely used methods for determining actual evapotranspiration through remote sensing. However, assuming a linear relationship between temperature difference (dT) and land surface temperature (LST) and the visual identification of hot and cold pixels are the weak points of the method. Modified SEBAL (M-SEBAL) on the other hand, replaces two new concepts as hot and cold edges, with a significant impact on increasing the accuracy and automation of the execution of the algorithm and removing the need for skilled user. Although, determining the warm edge requires high computing and thus increase in computing time and possible errors. In this study, a simplified form of the M-SEBAL (SM-SEBAL) is developed and the results of executing of the three method on MODIS images and comparing the results with the observed data are shown. Results show that SEBAL overstimates the ET values and M-SEBAL and SM-SEBAL underestimate the same values. The highest sensible heat flux calculation error comes from M-SEBAL (33.87%) and SEBAL shows the lowest error rate (11.23%). The highest error in the calculation of the latent heat (LE) retrives from M-SEBAL (11.66%) and the lowest error rate retrives from SEBAL (5.05%). However, the maximum daily evapotranspiration observed error was for SEBAL (3.56%) and the lowest error was retrived from SM-SEBAL (1.81%) and then M-SEBAL (2.47%). In addition, the time required to perform SEBAL algorithm is up to four times more than the time needed to run other two algorithms.

کلیدواژه‌ها [English]

Remote sensing, evapotranspiration, SEBAL, SM-SEBAL

مراجع

منابع

1)       Ahmad, M. D., Biggs, T., Turral, H., & Scott, C. A. (2006). Application of SEBAL approach and MODIS time-series to map vegetation water use patterns in the data scarce Krishna river basin of India. Water science and technology,53(10), 83-90.

2)       Allen, R. G., Tasumi, M., & Trezza, R. (2006, August). Benefits from tying satellite-based energy balance to reference evapotranspiration. In Earth Observation for Vegetation Monitoring and Water Management(AIP Conference Proceedings) (Vol. 852, pp. 127-137).

3)       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.

4)       Allen, R., Irmak, A., Trezza, R., Hendrickx, J. M., Bastiaanssen, W., & Kjaersgaard, J. (2011). Satellite‐based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes, 25(26), 4011-4027.

5)       Allen, R. G., Morse, A., Tasumi, M., Bastiaanssen, W., Kramber, W., & Anderson, H. (2001). Evapotranspiration from Landsat (SEBAL) for water rights management and compliance with multi-state water compacts. In Geoscience and Remote Sensing Symposium, 2001. IGARSS'01. IEEE 2001 International(Vol. 2, pp. 830-833). IEEE.

6)       Al Zayed,I.S., Elagib, N.A., Ribbe, L., and Heinrich, L. (2016). Satellite-based evapotranspiration over Gezira Irrigation Scheme, Sudan: A comparative study. Agricultural Water Management 177: 66–76.

7)       Akbari, M., Toomanian, N., Droogers, P., Bastiaanssen, W.G.M., Gieske, A., 2007. Monitoring irrigation performance in Esfahan, Iran, using NOAA satellite imagery. Agric. Water Manage. 88, 99–109.

8)       Bandara KMPS (2006)Assessing irrigation performance by using remote sensing. Doctoral thesis, Wageningen University, Wageningen, The Netherlands

9)       Bastiaanssen, W.G.M.; Menenti, M.; Feddes, R.A.; Holtslag, A.A.M. A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. J. Hydrol. 1998a, 212–213, 198–212.

10)   Bastiaanssen, W.G.M.; Pelgrum, H.; Wang, J.; Ma, Y.; Moreno, J.F.; Roerink, G.J.; van der Wal, T. A remote sensing surface energy balance algorithm for land (SEBAL): 2. Validation. J. Hydrol. 1998b, 212–213, 213–229.

11)   Bastiaanssen,  W.G.M.,  Hoekman,  D.H.  &  Roebeling,  R.A.  1994. A  methodology  for  the assessment of surface resistance and soil water storage variability at mesoscale based on remote  sensing  measurements,  a  case  study with HAPEX-EFEDA data, IAHS  Special Publications no. 2, IAHS Press, Institute of Hydrology, Wallingford, UK: pp. 66.

12)   Bastiaanssen, W.G.M., E.J.M. Noordman, H. Pelgrum, G. Davids, B.P. Thoreson, and R.G. Allen. 2005. SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. Journal of irrigation and drainage engineering 131:85-93.

13)   Bastiaanssen, W. G. M., Wal, T. van der and Visser, T. N. M.: 1996, Diagnosis of regional evapo-transpiration by remote-sensing to support irrigation performance assessment, Irr. Drainage Syst.10(1), 1–24.

14)   Bastiaanssen, W.G.M. (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229, 87–100.

15)   Bastiaanssen, W.G.M., Ahmad, M.D. and Chemin, Y. (2002). Satellite surveillance of evaporative depletion across the Indus. Water Resources Research, 38(12), 1273, 1–9.

16)   Bhattarai, N., Shaw, S.B., Quackenbush, L.J., Im, J., and 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

17)   Chemin, Y., Platonov, A., Ul-Hassan, M., & Abdullaev, I. (2004). Using remote sensing data for water depletion assessment at administrative and irrigation-system levels: case study of the Ferghana Province of Uzbekistan. Agricultural Water Management, 64(3), 183-196.

18)   Dorji, M. (2003). Integration of SWAP model and SEBAL for evaluation of on-farm irrigation scheduling with minimum field data. Enschede, ITC, 100.

19)   Droogers, P., & Bastiaanssen, W. (2000). Combining remote sensing and hydrological models to enhance spatial and temporal variability. IAHS-AISH PUBL., (267), 574-579.

20)   DuffieJ.A  and  W.A.Beckman.  1991.  Solar  engineering  of  thermal  process.  2nd Ed. John Wiley and sons, NY

21)   Evans, R., Bastiaanssen, W. G. M., Molloy, R., Hulbert, S., & Miltenburg, I. (2009, October). Improving the picture for irrigation using SEBAL in Australia to measure evapotranspiration (ET). In Irrigation and Drainage Conference 2009.

22)   El‐Magd, I. A., & Tanton, T. (2005). Remote sensing and GIS for estimation of irrigation crop water demand. International Journal of Remote Sensing, 26(11), 2359-2370.

23)   Engman, E.T. and Gurney, R.J. (1991). Remote Sensing in Hydrology, Chapman and Hall, London, UK.

24)   Farah, H.O. (2001). Estimation of regional evaporation using a detailed agro-hydrological model. Journal of Hydrology, 229(1–2), 50–58.

25)   French, A. N., Jacob, F., Anderson, M. C., Kustas, W. P., Timmermans, W., Gieske, A., ... & Brunsell, N. (2005). Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sensing of Environment, 99(1), 55-65.

26)   Guimarães Santos, C.A., da Silva, R.M., Silva, A.M., and Brasil Neto, R.M. (2017). Estimation of evapotranspiration for different land covers in a Brazilian semi-arid region: A case study of the Brígida River basin, Brazil. Journal of South American Earth Sciences, doi: 10.1016/j.jsames.2017.01.002.

27)   Granger, R.J. Satellite-derived estimates evapotranspiration in the Gediz basin. J. Hydrol. 2000, 229, 70–76.

28)   Hemakumara, H. M., Chandrapala, L., & Moene, A. F. (2003). Evapotranspiration fluxes over mixed vegetation areas measured from large aperture scintillometer. Agricultural water management, 58(2), 109-122.

29)   Hendrickx, J. M., & Hong, S. H. (2005, May). Mapping sensible and latent heat fluxes in arid areas using optical imagery. In Defense and Security (pp. 138-146). International Society for Optics and Photonics.

30)   Hong, S., Hendrickx, J. M., & Allen, R. G. (2008, December). Comparison of Remote Sensing Energy Balance Models: Sebal VS Metric. In AGU Fall Meeting Abstracts (Vol. 1, p. 1094).

31)   Immerzeel, W.W., Gaur, A., Zwart, S.J., 2008. Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a South Indian catchment. Agric. Water Manage. 95 (1), 11–24.

32)   Jian-ying, Y., Xu-rong, M., Zhi-guo, H., Chang-rong, Y., Hui, J., Feng-hua, Z., and Qin, L. (2015). Water consumption in summer maize and winter wheat cropping system based on SEBAL model in Huang-Huai-Hai Plain, China. Journal of Integrative Agriculture , 14(10): 2065–2076.

33)   Keshavarz, M. R., Vazifedoust, M., Alizadeh, A., Ansari, H., and Davari, K. (2011). Using S-SEBI and remote sensing to retrieve ET from vegetated lands. case study: Isfahan. Journal of lrrigation engineering (11):11-22. In Farsi.

34)   Kleissl, J., S.-H. Hong, and J. M. H. Hendrickx (2009), New Mexico scintillometer network supporting remote sensing and hydrologic and meteorological models, Bull. Amer. Meteorol. Soc. 90(2), 207–218.

35)   Kite, G., and Droogers, P. 2000. Comparing evapotranspiration estimates from satellites, hydrological models and field datga. J Hydrol. 229: 3–18.

36)   Kustas WP, French AN, Hatfield JL, Jackson TJ, Moran MS, Rango A, Ritchie JC, Schumgge TJ (2003) Remote sensing research in Hydrometeorology. Photogrammetric Eng Remote Sensing 69(6):631–646

37)   Kustas, W.P., Norman, J.M. Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrol. Sci. J. 1996, 41, 495–516.

38)   Kustas, W.P., Norman, J.M. Anderson, M. C., French, A.N. (2003) Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Journal of Remote Sensing Environment. 85: 429-440.

39)   Li, Z., Liu, X., Ma, T., Kejia, D., Zhou, Q., Yao, B., and Niu, T. (2013). Retrieval of the surface evapotranspiration patterns in the alpine grassland–wetland ecosystem applying SEBAL model in the source region of the Yellow River, China. Ecological Modelling 270: 64–75

40)   Liou, Y. and Kar, S.K., Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review, Energies 2014, 7, 2821-2849;

41)   Long, D., & Singh, V. P. (2010). Integration of the GG model with SEBAL to produce time series of evapotranspiration of high spatial resolution at watershed scales. Journal of Geophysical Research: Atmospheres (1984–2012), 115(D21).

42)   Long, D., & Singh, V. P. (2012). A modified surface energy balance algorithm for land (M‐SEBAL) based on a trapezoidal framework. Water Resources Research, 48(2).

43)   Long, D., Singh, V. P., & Li, Z. L. (2011). How sensitive is SEBAL to changes in input variables, domain size and satellite sensor?. Journal of Geophysical Research: Atmospheres (1984–2012), 116(D21)

44)   Mahmoud, S.H., and 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. (Article in press): http://dx.doi.org/10.1016/j.jseaes.2016.05.012

45)   Mallick, K., Bhattacharya, B. K., & Patel, N. K. (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology, 149(8), 1327-1342.

46)   Marx, A., H. Kunstmann, D. Schüttemeyer, and A. F. Moene (2008), Uncertainty analysis for satellite derived sensible heat fluxes and scintillometer measurements over Savannah environment and comparison to mesoscale meteorological simulation results, Agr. Forest Meteorol., 148(4),656–667.

47)   Melesse, A. M., and V. Nangia (2005), Estimation of spatially distributed surface energy fluxes using remotely sensed data for agricultural fields, Hydrol. Processes, 19(14), 2653–2670.

48)   Morse, A., Tasumi, M., Allen, R.G., Kramber, W.J., 2000. Application of the SEBAL Methodology for Estimating Consumptive Use of Water and Stream flow Depletion in the Bear River Basin of Idaho Through Remote Sensing [R]. Final report submitted to the Raytheon Systems Company, Earth Observation System Data and Information System Project, by Idaho Department of Water Resources and University of Idaho.

49)   Norman, J.M.; Anderson, M.C.; Kustas, W.P. Are single-source, remote-sensing surface-flux models too simple? In Earth Observation for Vegetation Monitoring and Water Management, D'Urso, G., Osann, Jochum, M.A., Moreno, J., Eds. American  Institute  of  Physics:  Melville, New York, USA, 2006; Volume 852, pp. 170-177.

50)   Oki, T. and Kanae, S., (2006) Global hydrological cycles and world water resources. Science, 313, 1068–1072,.

51)   Opoku-Duah, S.; Donoghue, D.N.M.; Burt, T.P. (2008) Intercomparison of evapotranspiration over the Savannah Volta Basin in West Africa using remote sensing data. Sensors, 8, 2736-2761.

52)   Papadavid, G., Hadjimitsis, D. G., Toulios, L., & Michaelides, S. (2013). A modified SEBAL modeling approach for estimating crop evapotranspiration in semi-arid conditions. Water resources management, 27(9), 3493-3506.

53)   Patel, N. R., Rakhesh, D., & Mohammed, A. J. (2006). Mapping of regional evapotranspiration in wheat using Terra/MODIS satellite data. Hydrological sciences journal, 51(2), 325-335.

54)   Paul, G., Gowda, P. H., Vara Prasad, P. V., Howell, T. A., Staggenborg, S. A., & Neale, C. M. (2013). Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08. Advances in Water Resources, 59, 157-168.

55)   Paul, G., Gowda, P.H., Vara Prasad, P.V., Howell, T.A., Aiken, R.M., and Neale, C.M.U. (2014). Investigating the influence of roughness length for heat transport (zoh) on the performance of SEBAL in semi-arid irrigated and dryland agricultural systems. Journal of Hydrology 509: 231–244.

56)   Pelgrum, H. and Bastiaanssen, W. G.M.: 1996, An intercomparison of techniques to determine the area-averaged latent heat flux from individual in situ observations: a remote-sensing approach using the EFEDA data, Water Resour. Res. 32(9), 2775–2786.

57)   Roerink, G.J., Bastiaanssen, W.G.M., Chambouleyron, J., Menenti, M., 1997. Relating crop water consumption to irrigation water supply by remote sensing. Water Resour. Manag. 11 (6), 445–465.

58)   Ruhoff, A. L., Paz, A. R., Collischonn, W., Aragao, L. E., Rocha, H. R., & Malhi, Y. S. (2012). A MODIS-based energy balance to estimate evapotranspiration for clear-sky days in Brazilian tropical savannas. Remote Sensing, 4(3), 703-725.

59)   Sari, D.K., Ismullah, I.H., Sulasdi, W.N., and Harto, A.B. (2013). stimation of water consumption of lowland rice in tropical area based on heterogeneous cropping calendar using remote sensing technology. Procedia Environmental Sciences 17:  298 – 307.

60)   Senay, G.B., Budde, M., Verdin, J.P., Melesse, A.M., (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors 7 (6), 979–1000. http://dx. doi.org/10.3390/s7060979.

61)   Senay, G.B., Bohms, S., Singh, R.K., Gowda, P.H., Velpuri, N.M., Alemu, H., Verdin, J.P. (2013). Operational evapotranspiration mapping using remote sensing and weather datasets: a new parameterization for the SSEB approach. J. Am. Water Resour. Assoc. 49 (3), 577–591. http://dx.doi.org/10.1111/jawr.12057.

62)   Singh, R. K., A. Irmak, S. Irmak, and D. L. Martin (2008), Application of SEBAL model for mapping evapotranspiration and estimating surface energy fluxes in south-central Nebraska, J. Irrig. Drain. Eng., 134(3),273–285.

63)   Spiliotopoulos M, Loukas A, Vasiliades L (2008)Actual evapotranspiration estimation from satellite-based surface energy balance model in Thessaly, Greece. EGU General Assembly, 13-18 April 2008, Vienna, Austria, Geophysical Research Abstracts: Vol 10

64)   Su, Z. A Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes from point  to  continental  scale.  In  Advanced  Earth  Observation—Land  Surface  Climate;  Su,  Z., Jacobs,  J.,  Eds.;  Publications  of  the  National  Remote  Sensing  Board  (BCRS):  Delft,  The Netherlands, 2001; Volume 01–02, pp. 91–108.

65)   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), 1086-1092.

66)   Tang, R., and Li, Z.L. (2015). Evaluation of two end-member-based models for regional land surface evapotranspiration estimation from MODIS data Agricultural and Forest Meteorology 202: 69–82.

67)   Tasumi, M., Trezza, R., Allen, R.G. and Wright, J.L. (2003). U.S. Validation tests on the SEBAL model for evapotranspiration via satellite. ICID Workshop on Remote Sensing of ET for Large Regions, Montpellier, France, 17 Sept. 2003.

68)   Teixeira, A.H.D.C.; Bastiaanssen, W.G.M.; Ahmadd, M.D.; Bos, M.G. Reviewing SEBAL input parameters  for  assessing  evapotranspiration  and  water  productivity  for  the  Low-Middle  São Francisco River basin, Brazil Part A:  Application  to  the  regional  scale.  Agric.  For.  Meteorol. 2009a, 149, 462-476.

69)   Teixeira, A.H.D.C.; Bastiaanssen, W.G.M.; Ahmadd, M.D.; Bos, M.G. Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle São Francisco river basin, Brazil art B: Application to the regional scale.  Agric.  For.  Meteorol. 2009b, 149, 477-490.

70)   Thoreson, B., Clark, B., Soppe, R., Keller, A., Bastiaanssen, W., & Eckhardt, J. (2009, May). Comparison of evapotranspiration estimates from remote sensing (SEBAL), water balance, and crop coefficient approaches. InProceedings ASCE World Environmental and Water Resources Congress 2009: Great Rivers.

71)   Timmermans, W.J., Meijerink, A.M.J., 1999. Remotely sensed actual evapotranspiration: implications for groundwater management in Botzwana. J. Appl. Geohydrol. 1 (3/4), 222–233.

72)   Timmermans, W. J., Kustas, W. P., Anderson, M. C., & French, A. N. (2007). An intercomparison of the surface energy balance algorithm for land (SEBAL) and the two-source energy balance (TSEB) modeling schemes. Remote Sensing of Environment, 108(4), 369-384.

73)   Trezza R (2002) Evapotranspiration using a satellite-based surface energy balance with standardized ground control. PhD Disseration, Biological and Irrigation Engineering Department, Utah State University, Logan

74)   Van den Hurk, B. J. J. M. (2001). Energy balance based surface flux estimation from satellite data, and its application for surface moisture assimilation. Meteorology and Atmospheric Physics, 76(1-2), 43-52.

75)   Van Eekelen, M.W., Bastiaanssen, W.G.M., Jarmain, C., Jackson, B., Ferreira, F., van der Zaag, P., Saraiva Okello, A., Bosch, J., Dye, P., Bastidas-Obando, E., Dost, R.J.J., and Luxemburg, W.M.J. (2015). A novel approach to estimate direct and indirect water withdrawals from satellite measurements: A case study from the Incomati basin. Agriculture, Ecosystems and Environment 200:126–142

76)   Wang, J., Bastiaanssen, W.G.M., Ma, Y., Pelgrum, H., 1998. Aggregation of land surface parameters in the oasis-desert systems of Northwest China. Hydrol. Process. 12, 2133–2147.

77)   Wang, J., Gao, F., Liu, S., 2003. Remote sensing retrieval of evapotranspiration over the scale of drainage basin. Remote Sensing Technology and Application 18 (5), 332–338 (in Chinese).

78)   Wang J, Sammis T.W, Meier C.A, Simmons L.J, Miller D.R, Samani Z, 2006, A Modified SEBAL model for spatially estimating pecan consumptive water use for las cruces, New Mexico, Agronomy and Horticaulture Departement, New Mexico State Univ. Las Cruses, New Mexico, USA.

79)   Wu, C. D., Cheng, C. C., Lo, H. C., & Chen, Y. K. (2010). Application of SEBAL and Markov models for future stream flow simulation through remote sensing. Water resources management, 24(14), 3773-3797.

80)   Xiong, J., Wu, B., Zhou, Y., Li, J., 2006. Estimating evapotranspiration using remote sensing in the Haihe Basin. In: Geoscience and Remote Sensing Symposium, IGARSS IEEE International Conference. pp. 1044–1047.

81)   Zeng, L., Song, K., Zhang, B., Wang, Z., 2010, SPATIAL MAPPING OF ACTUAL EVAPOTRANSPIRATION AND WATER DEFICIT WITH MODIS PRODUCTS IN THE SONGNEN PLAIN, NORTHEAST CHINA, IGARSS IEEE International Conference. pp. 879–882.

82)   Zhou, X., Bi, S., Yang, Y., Tian, F., and Ren, D. (2014). Comparison of ET estimations by the three-temperature model, SEBAL model and eddy covariance observations. Journal of Hydrology 519: 769–776.