تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,503 |
تعداد مشاهده مقاله | 124,120,190 |
تعداد دریافت فایل اصل مقاله | 97,227,028 |
تاثیر بهکارگیری الگوریتمهای مختلف دمای سطح زمین در برآورد مقادیر تبخیر-تعرق واقعی | ||
تحقیقات آب و خاک ایران | ||
دوره 53، شماره 12، اسفند 1401، صفحه 2701-2720 اصل مقاله (2.13 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2022.351202.669396 | ||
نویسندگان | ||
آرین حیدری مطلق1؛ علی حیدر نصرالهی* 2؛ شادمان ویسی3؛ مجید شریفی پور4 | ||
1گروه آبیاری و زهکشی، گروه مهندسی آب، دانشگاه شهید چمران اهواز | ||
2دانشگاه لرستان- استادیار گروه مهندسی آب دانشکده کشاورزی و منابع طبیعی | ||
3موسسه تحقیقات خاک و آب، بخش آبیاری و فیزیک خاک، کرج | ||
4استادیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه لرستان. لرستان، ایران. | ||
چکیده | ||
یکی از روشهای مناسب بهمنظور برآورد تبخیر-تعرق واقعی، استفاده از فن سنجش از دور است که به دلیل پوشش مکانی و زمانی مناسب، گزینه خوبی برای اندازهگیری در سطح گسترده به حساب میآید. هدف از پژوهش حاضر، برآورد تبخیر-تعرق واقعی با استفاده از الگوریتم سبال و به کارگیری الگوریتمهای تابع پلانک و پنجره مجزا برای محاسبه تاثیر پارامتر دمای سطح و مقایسه روشهای مختلف برآورد دمای سطح و مشاهده تاثیر آن بر مقادیر تبخیر-تعرق واقعی است. برای این منظور، اطلاعات میدانی شامل دمای پوشش سبز در سطح مزرعه و اندازهگیری حجم آب ورودی و خروجی در مقیاس لایسیمتر درمزرعه تحت کشت یونجه در سال زراعی 99-1398 همزمان با روزهای گذر ماهواره لندست 8 برفراز محدوده مطالعاتی در نقاط از قبل تعیین شده در سطح مزرعه برداشت شد. پس از انجام پیش پردازشهای لازم روی تصاویر ماهوارهای، ابتدا با استفاده از باندهای حرارتی و دو الگوریتم پنجره مجزا و تابع پلانک، دمای مزراع تخمین زده شد. نتایج نشان داد در هر گذر با دمای پوشش گیاهی اندازهگیری شده با استفاده از دماسنج مادون قرمز، الگوریتم پنجره مجزا مقادیر همبستگی بالاتری نسبت به روش تابع پلانک به میزان 68 تا 80 درصد داشت. در مرحله بعد به برآورد تبخیر-تعرق با استفاده از الگوریتم سبال تحت دو سناریوی دمای تابع پلانک و پنجره مجزا پرداخته شد. مقایسه نتایج تبخیر-تعرق واقعی محاسبه شده با لایسیمتر نشان داد که پیکسل سرد بیشترین انطباق را با نحوه آبیاری در لایسیمتر دارد، که پیکسل سرد حاصل از الگوریتم پنجره مجزا با میلیمتر در روز 56/0RMSE=، 084/0nRMSE= و 992/0NS=، بیشترین مطابقت را با دادههای لایسیمتر دارد. همچنین بر اساس شاخص rMBE الگوریتم پنجره مجزا با کمبرآوردی در بازه 07/4- تا 22/3- درصد بوده در حالیکه الگوریتم تابع پلانک با بیشبرآوردی در بازه 76/4 تا 65/12 درصد در نوسان بوده است. این بررسی فقط اختصاص به پیکسل سرد ماهواره با شرایط بدون تنش آبی بوده و برای بررسیهای بیشتر نیازمند ابزار دقیق میباشد. | ||
کلیدواژهها | ||
پیکسل سرد؛ سنجش از دور؛ الگوریتم سبال؛ لایسیمتر | ||
عنوان مقاله [English] | ||
The influence of land surface temperature (LST) on estimated actual evapo transpiration | ||
نویسندگان [English] | ||
aryan heidari motlagh1؛ aliheidar nasrolahi2؛ shadman veysi3؛ Majid Sharifipour4 | ||
1Department of Irrigation and Drainage, Department of Water Engineering, Shahid Chamran University of Ahvaz | ||
2Lorestan University _ Assistant Professor, Department of Water Engineering, Faculty of Agriculture and Natural Resources | ||
3Soil and Water Research Institute, Department of Irrigation and Soil Physics, Karaj | ||
4Department of Water Engineering, Faculty of Agriculture, Lorestan University, Lorestan, Iran. | ||
چکیده [English] | ||
The remote sensing technique is a suitable method for estimating actual evapotranspiration (ETa) at the large-scale due to spatial and temporal resolution. The present study aims to assess the ETa using the SEBAL and different algorithms to survey the effect of the LST and their impact assessment on the ETa fluctuation. Field measurement, including canopy temperature and the volume of inflow and outflow of water consumption was done based on lysimeters during 2018-2019. After the necessary pre-processing on the satellite images, the Land Surface Temperature (LST) was estimated using Planck's and split window algorithms. The result showed that the performance of Split window was better than to the Planck algorithm. Also, ETa was estimated by the SEBAL algorithm based on two temperature scenarios including the Planck and split window. The results showed, the cold pixel of SEBAL algorithm had compliance with the Lysimetric measurement. Moreover, the cold pixel of the split window algorithm with RMSE=0.56, NRMSE=0.084 and NS=0.992 (mm/day) had the highest consistency with the lysimeter data. Also, the rMBE index of the split window algorithm was associated with underestimation in the range of -4.07 to -3.22%, while the Planck function algorithm fluctuated with overestimation in the range of 4.76 to 12.65%. This research has been verified to the cold pixel of satellite for crop with no stress conditions and for better investigation at crop stress condition, precise instruments are needed. | ||
کلیدواژهها [English] | ||
cold pixel, remote sensing, SEBAL algorithm, Lysimetric | ||
مراجع | ||
Abrishamkar, M., Ahmadi, A., (2017). Evapotranspiration estimation using remote sensing technology based on SEBAL algorithm. Iran. J. Sci. Technol. Trans. Civ. Eng. 41, 65–76. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., (1998). Crop Evapotranspiration-guidelines for Computing Crop Water requirements-FAO Irrigation and Drainage Paper 56. FAO, Rome 300 D05109. Allen, R.G., Pereira, L.S., Howell, T.A., Jensen, M.E., (2011). Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agric. Water Manag. 98,899–920. Awada, H., Di Prima, S., Sirca, C., Giadrossich, F., Marras, S., Spano, D., & Pirastru, M. (2022). A remote sensing and modeling integrated approach for constructing continuous time series of daily actual evapotranspiration. Agricultural Water Management, 260, 107320. Bastiaanssen, W.G., Menenti, M., Feddes, R., Holtslag, A., (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. (Amst) 212,198–212. Blaney, H.F., (1952). Determining Water Requirements in Irrigated Areas From Climatological and Irrigation Data. Bispo, R. C., Hernandez, F. B. T., Gonçalves, I. Z., Neale, C. M. U., & Teixeira, A. H. C. (2022). Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach. Agricultural Water Management, 271, 107763. Calcagno, G., Mendicino, G., Monacelli, G., Senatore, A., Versace, P., (2007). Distributed estimation of actual evapotranspiration through remote sensing techniques. Methods and Tools for Drought Analysis and Management. pp. 125–147. Doorenbos, J. (1984). Guidelines for Predicting Crop Water Requirement Irrigation and Drainage paper 24. Food and Agriculture Organization of the United Nations, Rome. Ding, R., Kang, S., Zhang, Y., Hao, X., Tong, L., Du, T., (2013). Partitioning evapotranspiration into soil evaporation and transpiration using a modified dual crop coefficient model in irrigated maize field with ground-mulching. Agric. Water Manag127, 85–96. Droogers, P., Allen, R.G., (2002). Estimating reference evapotranspiration under inaccurate data conditions. Irrig. Drain. Syst. 16, 33–45. Du, Z., Li, W., Zhou, D., Tian, L., Ling, F., Wang, H., Gui, Y., Sun, B., (2014). Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sens. Lett. 5,672–681. Evcen, A., & YAĞCI, A. L. Gerçek Evapotranspirasyonun Landsat Uydu Görüntüleri Kullanarak SEBAL Modeli ile Hesaplanması: Bolu/Yeniçağa Örneği. Turkish Journal of Remote Sensing and GIS, 3(2), 172-182. 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 Resour. Manag. 25, 2731–2742. Ebrahimi Heravi, B., Rangzan,K., Riahi Bakhtiari H. R. & Taghi Zadeh A.(2016).Introducing the Most Appropriate Method to Extract Land Surface Temperature Using Landsat 8 Satellite Images in Karaj Metropolitan.Iranian Journal of Remote Sencing & GIS, Volume:8 Issue: 3. 59 - 76 (In Persian). Gonçalves, I. Z., Ruhoff, A., Laipelt, L., Bispo, R. C., Hernandez, F. B. T., Neale, C. M. U., ... & Marin, F. R. (2022). Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil. Agricultural Water Management, 274, 107965. heidari motlagh, A., nasrolahi, A., Sharifipour, M., vayci, S. (2021). 'Evaluation of Different Models for Estimating Reference Evapotranspiration (ETo) in Aleshtar Plain', Iranian Journal of Soil and Water Research. (In Persian). Jimenez-Munoz, J., C., Sobrino, J. A., Skokovic, D., Mattar, C., & Cristobal, J. (2014). Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, 11(10), 1840–1843 Jalilvand, E., Tajrishy, M., Hashemi, S. A. G. Z., & Brocca, L. (2019). Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sensing of Environment, 231, 111226. Jouybari,Y., Akhoondzadeh, M., & Saradjian, M., R. (2015) .A Split- Window Algorithm for Estimating LST from Landsat-8 Satellite Images. Journal of Geomatics Science and Technology. Volume 5 (1). 215-226 (In Persian). Liu, J., Chen, J., Cihlar, J., (2003). Mapping evapotranspiration based on remote sensing: an application to Canada’s landmass. Water Resour. Res. 39. Liou, Y.-A., Kar, S.K., (2014). Evapotranspiration estimation with remote sensing and various surface energy balance algorithms—a review. Energies 7, 2821–2849. Li Z-L, Tang B-H, Wu H, Ren H, Yan G, Wan Z, Trigo IF, Sobrino JA. (2013). Satellitederived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131: 14-3 Li, H.-j., Lei, Y.-p., Zheng, L., MAO, R.-z., (2005). SEBAL model and its application in the study of regional evapotranspiration. Remote Sens. Technol. Appl. 3, 003. Mao, Y., Wang, K., (2017). Comparison of evapotranspiration estimates based on the surface water balance, modified Penman‐Monteith model, and reanalysis data sets for continental China. J. Geophys. Res. Atmos. 122, 3228–3244. Montanaro, M., Gerace, A., Lunsford, A., Reuter, D., (2014). Stray light artifacts in imagery from the Landsat 8 Thermal Infrared Sensor. Remote Sens. (Basel) 6, 10435–10456. Ma Y, Kuang Y Q, Huang N S. (2010). Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery. International Journal of Applied Earth Observation and Geoinformation, 12(2): 110–118. Miller, W. and Millis, E. (1989). Estimating evaporation from Utah's Great Salt Lake using thermal infrared satellite imagery. Water Resources Bulletin, 25: 541-550 Nouri, H., Beecham, S., Kazemi, F., Hassanli, A.M., (2013). A review of ET measurement techniques for estimating the water requirements of urban landscape vegetation. Urban Water J. 10, 247–259. Olioso, A., Inoue, Y., Ortega-Farias, S., Demarty, J., Wigneron, J.-P., Braud, I., Jacob, F., Lecharpentier, P., Ottle, C., Calvet, J.-C., (2005). Future directions for advanced evapotranspiration modeling: assimilation of remote sensing data into crop simulation models and SVAT models. Irrig. Drain. Syst. 19, 377–412. Penman, H.L., (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society. pp. 120–145. Pinter Jr, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S., Upchurch, D.R., (2003). Remote sensing for crop management. Photogramm. Eng. Remote Sens. 69, 647–664. Petitcolin, F., Vermote, E. (2002). Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data. Remote Sensing of Environment, 83(1): 112-134. Peng S-S, Piao S, Zeng Z, Ciais P, Zhou L, Li LZ, Myneni RB, Yin Y, Zeng H. (2014). Afforestation in China cools local land surface temperature. Proceedings of the National Academy of Sciences, 111(8): 2915-2919. Qin, Z., Zhang, M., Arnon, K. (2001). Split window algorithms for retrieving land surface temperature from NOAA-AVHRR data. Remote Sensing For Land & Resources, 56(2): 33-42. Qurbani, O., Faramarezi, M., Kerami, J., Gholami, N., Sobhani, B. (2014). Comparative evaluation of Sabal and metric algorithms in estimating evaporation and transpiration: a case study of Malair city. Space planning and preparation 2014; 19 2:184-153. (In Persian). Running, S.W., Mu, Q., Zhao, M., Moreno, A. (2017). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3) NASA Earth Observing System MODIS Land Algorithm. Rahimian, M., Shayannejad, M., Islamian, S., gaysari,m ،. Jafari, R., Taqwaian, p. (2017). Evaluation of Earth's surface energy power algorithm to determine the actual evapotranspiration of pistachio trees under salinity and drought conditions: PhD thesis. Isfahan University of Technology. (In Persian). 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 Sens. Environ. 185, 171–185. Serbina, L., Miller, H.M., (2014). Landsat and water: case studies of the uses and benefits of Landsat imagery in water resources. US Geol. Survey Open-File Report 1108, 61. sane, M., Kouchakzadeh, M., & sharifi, F. (2020). Evaluation of SEBAL Algorithm for Estimation of Real Evapotranspiration in Vardij area. Iranian Journal of Irrigation & Drainage, 14(1), 125-135. (In Persian).
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. Math. Comput. Model. 54, 1086–1092. Singh, R.K., Irmak, A., Irmak, S., Martin, D.L., (2008). Application of SEBAL model for mapping evapotranspiration and estimating surface energy fluxes in south-central Nebraska. J. Irrig. Drain. Eng. 134, 273–285. Sobrino, J. A., Jiménez-Muñoz, J. C., El-Kharraz, J., Gómez, M., Romaguera, M., & Soria, G. (2004). Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site. International Journal of Remote Sensing, 25(1), 215-230. Sellers, P., Randall, D., Collatz, G., Berry, J., Field, C., Dazlich, D., Zhang, C., Collelo, G., Bounoua, L., (1996). A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: model formulation. J. Clim. 9, 676–705. Su, Z., Yacob, A., Wen, J., Roerink, G., He, Y., Gao, B., Boogaard, H., van Diepen, C., 45. (2003). Assessing relative soil moisture with remote sensing data: theory, experimental validation, and application to drought monitoring over the North China Plain. Phys. Chem. Earth Parts A/b/c 21, 14–101. Srivastava, P, K. Majumdar, T.J. Bhattacharya, Amit K. (2009). Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Advances in Space Research, 43 (10): 1563-1574. Thornthwaite, C.W., (1948). An approach toward a rational classification of climate. Geogr. Rev. 38, 55–94 Venkatram, A., (1980). Estimating the Monin-Obukhov length in the stable boundary layer for dispersion calculations. Boundary Meteorol. 19, 481–485. Veysi, S., Naseri, A. A., & Hamzeh, S. (2020). Relationship between field measurement of soil moisture in the effective depth of sugarcane root zone and extracted indices from spectral reflectance of optical/thermal bands of multispectral satellite images. Journal of the Indian Society of Remote Sensing, 48(7), 1035-1044. Veysi, S., Naseri, A., Hamzeh, S., Moradi, P. (2016). Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images. Journal of RS and GIS Techniques for Natural Resources, 7(1): 27-40. (In Persian). Wang, F., Qin, Zh. Song, C. Tu, L. Karnieli, A. Zhao, Sh. (2015). An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data. J. Res. doi: 10.3390/rs70404268. Zhu, A., J. Zhang, B. Zhao, Z. Cheng and L. Li. 2005. Water balance and nitrate leaching losses under intensive crop production with Ochric Aquic Cambosols in North China Plain. Environment International 31, 904 – 912. | ||
آمار تعداد مشاهده مقاله: 416 تعداد دریافت فایل اصل مقاله: 362 |