بررسی وضوح فضایی نقشه های تبخیر و تعرق واقعی در حوضه زاینده رود

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری بیابان زدایی، دانشکده منابع طبیعی وعلوم زمین، دانشگاه کاشان

2 دانشیار گروه بیابان زدایی، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان

3 استادیار گروه مهندسی طبیعت، دانشکده منابع طبیعی وعلوم زمین، دانشگاه شهرکرد

4 استادیار گروه جغرافیا و اکوتوریسم، دانشکده منابع طبیعی وعلوم زمین، دانشگاه کاشان

10.22131/sepehr.2020.40475

چکیده

تبخیر و تعرق به عنوان عامل مهم در اتلاف آب در مناطق خشک و نیمه خشک، پدیده پیچیدهای است کهبه عوامل و دادههای زیادی بستگی دارد. بنابراین برآورد دقیق میزان آن، بسیار مشکل وپرهزینه است.هدف از این مطالعه، بررسی اثرات ریزمقیاس نمایی کوکریجنگ دمای سطح زمین (LST)،برای برآورد تبخیر و تعرق واقعی (AET)، در ژوئن 2017 در حوضه زاینده رود است. در این راستا،در روش اول،ریزمقیاس نمایی کوکریجینگ به محصول LST حاصل از ماهواره MODIS اعمال شد. سپس با استفاده از سیستم  بیلان انرژی سطح (SEBS)،AET روزانه با وضوح متوسط ​​(250 متری) به دست آمد. در روش دوم، نقشه AET به وضوح متوسط ​​(250 متری) ریزمقیاس نمایی شد. اعتبار سنجی با استفاده از محصولات حاصل ازLandsat 8 صورت پذیرفت.
نتایج نشان داد مقادیر میانگین AET-SEBS ریزمقیاس نمایی (12/56mm/day)وAET مرجع (13/11mm/day) دارایاختلاف ناچیزهستند. RMSEمیانAETمرجع و AETریزمقیاس نمایی شده برابر با 1/66 میلیمتر/روز
(r = 0/73) و میانLSTمرجع و ریزمقیاس‌ نمایی شده معادل 4/36K و (r=0/78)  بود. این مطالعه نشان داد که مقادیر AET حاصله از دو روش ریزمقیاس نمایی، مشابه یکدیگر هستند، اما AET بدست آمده از LST ریزمقیاس نمایی شده، یک تغییرپذیری فضایی بالاتری را از خود نشان میدهد. مقایسه AET-SEBS با AET حاصل از روش پنمن- مانتیث- فائو نشان دهنده RMSE برابر با 26/2است. بنابراینLST اثر زیادی در تولید نقشههای AET از روی تصاویر سنجش از دور دارد و ریزمقیاس نمایی  کوکریجینگ برای ارائه  نقشههای AET روزانه  با  وضوح فضایی متوسط ​​مفید بوده است. در مجموع یافتههای پژوهش نشان داد با به کارگیری روش ریزمقیاس نمایی و SEBS، میتوان تبخیر و تعرق واقعی را در حوضه زاینده رود و برای مناطق خشک و نیمه خشک با دقت مطلوب محاسبه نمود.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the spatial resolution of actual evapotranspiration maps in the ZayandehRud basin

نویسندگان [English]

  • Mina Arast 1
  • Abolfazl Ranjbar 2
  • Khodayar Abdolahi 3
  • Sayed Hojjat Mousavi 4
1 Ph.D of Combat desertification, Department of desert control, Faculty of natural resources and earth sciences, University of Kashan, Iran
2 Associate professor, Department of desert control, Faculty of natural resources and earth sciences, University of Kashan, Iran
3 Assistant professor, Department of watershed management, Faculty of natural resources and earth sciences, Shahrekord University Iran
4 Assistant professor, Department of ecotourism, Faculty of natural resources and earth sciences, University of Kashan, Iran
چکیده [English]

Introduction
Evapotranspiration is one of the most important parts of the water cycle (Boegh and Soegaard 2004). Precise prediction of actual evapotranspiration () is essential for various fields, such as agriculture, water resource management, irrigation planning and plant growth modeling. Therefore, accurate determination of actual evapotranspiration has always been a major concern of experts in these fields. Due to the limited number of weather stations and the fact that collecting ground information is both time consuming and expensive, remote sensing and satellite imagerycan be a suitable tool in determination of actual evapotranspiration (Brisco et al., 2014). Satellite productions are usually divided into images with low, medium and high spatial resolution (Rao et al., 2017). Surface energy balance is a method usually used in combination withremotely sensed spatial data for estimation. Information collected from various sources, such as remotely sensedimageries and meteorological data, are used in this method. The present studyinvestigatesspatial distribution on different scales (from field- to regional-) using remotely sensed imagerieswithdifferent spatial and temporal resolution. TheSurface Energy Balance System (SEBS) is one of the most important methods used for the estimation of in remotely sensed images (Ochege et al., 2019). This model needs thermal maps produced using satellite images. Daily maps produced with RS are usually very large, and their pixelsize is usually so large that it can provide the spatial diversity found in the basins with respect to the errors (Mahour et al., 2017).
 
Material and Methods
In order to estimate the actual evapotranspirationin satellite images collected from Zayanderud basin,the effects of Co-Kriging downscaling of surface temperature (LST) were investigated in June 2017 using two different methods.To reach this aim, we first applied a co-kriging downscaling method to a low-power LST product collected from MODIS at 1000 meters. Then based on the results and using the SEBS system, the daily  was obtained from images with average spatial resolution (250 m).In the second method, map produced usinghigh resoultion satellite imageswas downscaled to medium resolution (250 m). For both methods, 250 m resolutionMODIS NDVI products were used as co-variables.Then, validation was performed using Landsat-8 imagery, and land surface temperature was extracted from its thermal bands. SEBS algorithm was used to determine in Landsat 8 30-meter resolutionimagery. Accuracy of measurements wasexamined based on a comparison between down scaledLST and maps (250 meterresolution).
 
Results and Discussion
In the present study, mean LST equals 3/312 K (SD = 1.74) and average daily equals 12.5 mm / day (SD = 0.86). In the downscaling phase, the relationship between LST parameters and and vegetation index(as a co-variable)was investigated.Moreover, to investigate the relation betweenhigh resolution variables and NDVI, we re-sampled LST and   variables from a 1000 mresolution to 250 mresolution.In250 mresolution, there is a negative linear relation (r=-0.85) between LST and NDVI, but the relation betweenand NDVI is positive (r = 0.80). Thus, lower LST (> 305k) indicates more vegetation (NDVI >0.3) inthe region, while higher LST results in lower NDVI or lack of vegetation. As a result, more vegetation can be observed in regions with higher(12 mm/day).
Results indicated that the difference between average  downscaled-SEBS (12.56 mm/day) and reference  (13.11 mm/day) is negligible. The RMSE between the reference and the downscaled  equaled 1.66 mm/day (r = 0.73), and RMSE between the reference LST and the downscaled LST equaled4.36 K (r = 0.78). Thus,values obtained from two downscaling methods were similar, but the  obtained from downscaled LST showed a higher spatial variation. Therefore, LST has greatly influenced the production of maps using remotely sensing images, and Co-Kriging downscaling has been useful for providing daily  maps with intermediate spatial resolution.
 
Conclusion
Evapotranspiration downscaling using the co-kriging method is not significantly different from the SEBS product and the results are similar. The results of -SEBS method isalso acceptable, but the  derived from the SEBS algorithm is more variable due to the LST downscaling.

کلیدواژه‌ها [English]

  • Actual evapotranspiration
  • Downscaling method
  • Remote Sensing
  • the SEBS product
  • The vegetation
1.   صالحی، ح.، شمس الدینی، ع.، میرلطیفی، س.م. 1397. ”ریزمقیاس نمایی تصویر مادیس به منظور تهیه نقشه تبخیرتعرق روزانه با قدرت تفکیک تصویر لندست با استفاده از الگوریتم‌‌های SADFDT و “STARFM. سنجش از دور و GIS ایران. 10(3): 140-123.

2.   عظیمی، ع.، رنگزن، ک.، کابلی زاده، م.، خرمیان، م. 1394. ”برآورد تبخیر و تعرق با استفاده از سنجش از دور، شبکه‌‌های عصبی مصنوعی و مقایسه نتایج آن با روش پنمن- مانتیث- فائو در باغات مرکبات شمال خوزستان“. سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی. 6(4): 75-61.

3.   محمد ابراهیم، مهدی.، محمدرضاپور، احمد.، اکبرزاده مقدم سه قلعه، هادی.، 1396.”ارزیابی مدل سبس در برآورد تبخیرتعرق واقعی با استفاده از تصاویر ماهواره‌ای مودیس در مقیاس منطقه‌ای (مطالعه موردی: دشت سیستان) “. مجله اکوهیدرولوژی، 4(4):1150-1141.

4.   Atkinson, P.M. 2013. “Downscaling in remote sensing,” Intenational Journal of Applied Earth Observation and Geoinformation, 22:106-114. https://doi.org/10.1016/j.jag.2012.04.012

5.   Brisco, B., R.J. Brown., T. Hirose., H. McNairn., K. Staenz. 2014 “Precision agriculture and the role of remote sensing: a review.” Can. J. Rem. Sens, 24:315–327. http://dx.doi.org/10.1080/07038992.1998.10855254

6.   Caroline, M., F.Gevaert., J. Gercia-Haro. 2015. “A comparison of STARFM and an unmixing based algorithm for Landsat and MODIS data fusion.” Remote Sensing of Environment, 156:34-44. http://dx.doi.org/10.1016/j.rse.2014.09.012

7.   Emelyanova, I.V., T.R. McVicar., T.G. Van Niel., L.T. Li., A.I.J. Van Dijk. 2013. “Assessing the accuracy of blending Landsat MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection.” Remote Sensing of Environment, 133:193-209. http://dx.doi.org/10.1016/j.rse.2013.02.007

8.   Gao, F., J. Masek., M. Schwaller., F. Hall. 2006. “On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance.” IEEE Transactions on Geoscience and Remote Sensing, 44(8):2207 2218. http://dx.doi.org/10.1109/TGRS.2006.872081

9.   Gowda, P.H., J.L. Chavez., P.D. Colaizzi., S.R. Evett., T.A. Howell., J.A. Tolk. 2007. “ET mapping for agricultural water management: present status and challenges.” Irrig. Sci, 26:223–237. http://dx.doi.org/10.1007/s00271-007-0088-6

10. Ha, W., P.H. Gowda., T.A. Howell. 2013 “A review of downscaling methods for remote sensing-based irrigation management.” Journal of Irrigation Science, 31: 831-850. http://dx.doi.org/10.1007/s00271-012-0331-7

11. Ha, W., H. Prasanna., P.H. Gowda ., A. Terry.,  T.A. Howell. 2013. “ Downscaling of Land Surface Temperature Maps in the Texas High Plains with the TsHARP Method." GIScience & Remote Sensing, 48(4):583-599.https://doi.org/10.2747/1548-1603.48.4.583

12. Ha, W., P.H. Gowda., T.A. Howell. 2012. “A review of potential image fusion methods for remote sensing-based irrigation management: part II.” Irrig. Sci, 31:851–869. http://dx.doi.org/10.1007/s00271-012-0340-6

13. Jimenez-Munoz, J.C., J.A. Sobrino., D. Skokovic., C. Mattar., J. Cristobal. 2014 “Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data.” IEEE Geosci. Rem. Sens. Lett, 11:1840–1843.  http://dx.doi.org/10.1109/LGRS.2014.2312032.

14. Lopes, J.D., L.N, Rodrigues., H.M, Acioli Imbuzeiro & F.F, Pruski. 2019. “Performance of SSEBop model for estimating wheat actual evapotranspiration in the Brazilian Savannah region.” International Journal of Remote Sensing, 40:18, 6930-6947. http://dx.doi.org/10.1080/01431161.2019.1597304

15. Mahour, M., V. Tolpekin., A. Stein., A. Sharifi. 2017 “A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration.” ISPRS Journal of Photogrammetry and Remote Sensing, 126:56-67. http://dx.doi.org/10.1016/j.isprsjprs.2017.02.004

16. Mahour, M., A. Stein., A. Sharifi., V. Tolpekin. 2015. “Integrating super resolution mapping and SEBS modeling for evapotranspiration mapping at the field scale.” Precis. Agric, 1–28. http://dx.doi.org/10.1007/s11119-015-9395-8.Matheron, G., 1963. Principles of geostatistics. Econ. Geol. 58, 1246–1266. http://dx. doi.org/10.2113/gsecongeo.58.8.1246.

17. Ochege, F.U., L. G. Luo., M.C. Obeta., G. Owusu., E. Duulatov., L. Cao. 2019. “Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL.” GIScience & Remote Sensing Published online: 16 Jul 2019. https://doi.org/10.1080/15481603.2019.1643531

18. Papoulis, A., Pillai, S.U., 2002. Probability, Random Variables, and Stochastic Processes.

19. Rao. M, Z. Silber-Coats., S. Powers., L. Fox III & A. Ghulam. 2017. “Mapping drought-impacted vegetation stress in California using remote sensing.” GIScience & Remote Sensing, 54:2, 185-201, DOI: 10.1080/15481603.2017.1287397

20. Ren, H., C. Du, Q. Qin., R. Liu., J. Meng., J. Li. 2014 “Atmospheric water vapor retrieval from Landsat 8 and its validation. In: 2014 IEEE Geoscience and Remote Sensing Symposium.” IEEE, 3045–3048.  http://dx.doi.org/10.1109/IGARSS.2014.6947119.

21. Rodriguez-Galiano, V., E. Pardo-Iguzquiza., M. Sanchez-Castillo., M. Chica-Olmo., M. Chica-Rivas. 2012. “Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images.” Int. J. Appl. Earth Obs. Geoinf, 18:515– 527. http://dx.doi.org/10.1016/j.jag.2011.10.002.

22. Roy, D.P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., et al. (2008) Multi-temporal MODIS Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. ” Remote Sensing of Environment, 112(6):3112 -3130. http://dx.doi.org/10.1016/j.rse.2008.03.009

23. 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, 979–1000. http://dx.doi.org/10.3390/s7060979.

24. Su, Z. 2002. “The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci, 6:85–100. http://dx.doi.org/10.5194/hess-6- 85-2002.

25. Szabó. S., L. Elemér.,  Z. Kovács., Z. Püspöki., A. Kertész., S. Kumar Singh & B. Balázs. 2019. “NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary.” GIScience & Remote Sensing, 56:4, 624-644, http://dx.doi.org 10.1080/15481603.2018.1560686

26.          Tang, Y., P.M. Atkinson.,  J. Zhang. 2015. “Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation.” ISPRS J. Photogramm. Rem. Sens, 101:174–185.  http://dx.doi.org/10.1016/j.isprsjprs.2014.12.016.