تخمین تبخیروتعرق واقعی براساس الگوریتم‌های تک منبعی و دومنبعی سنجش از دوری (مطالعه موردی: برازجان)

نوع مقاله : مقالات پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشگاه بین‌المللی امام خمینی (ره) .

2 دانشگاه بین المللی امام خمینی (ره)

چکیده

شناخت و ارزیابی تبخیر و تعرق از سطوح گیاهی یک ابزار اساسی در محاسبه بیلان آب و تخمین نیاز آبی و دسترسی به آن است. در این تحقیق به­منظور بررسی توزیع مکانی تبخیروتعرق و رابطه آن با سنجش از دور در مقابل داده­های لایسیمتری به عنوان شاهد در منطقه برازجان واقع در استان بوشهر، ایران انجام شد. در این پژوهش از 46 تصویر بدون ابر و روزانه از سنجنده MODIS، 7 تصویر از سنجنده ETM+ و 7 تصویر از سنجنده TM در طول فصل رشد از ماه فروردین تا  شهریور در خلال سال‌های 80 تا 82 استفاده شده است. براساس نتایج بدست آمده از اجرای سه مدل SEBAL، SSEB و TSEB در هر سه ماهواره، سنجنده MODIS دارای کمترین میزان خطا بوده (به‌ترتیب برای هر سه الگوریتم RMSE=0.856,1.385,2.7mm/day) و پس از آن ماهواره لندست 7 با قدرت تفکیک مکانی بالاتر در رده دوم قرار می­گیرد (به‌ترتیب برای هر سه الگوریتم RMSE=1.042,1.56,2.76 mm/day) و در نهایت ماهواره لندست 5 بیشترین میزان خطا را به خود اختصاص می­دهد (به­ترتیب برای هر سه الگوریتم RMSE=1.14, 1.97, 3.06 mm/day). در بررسی وضعیت پوشش گیاهی براساس شاخص نرمال شده تفاوت پوشش گیاهی، در ابتدای دوره کشت به دلیل جوانه­زنی و تنک بودن پوشش گیاهی، این شاخص در پایین­ترین حد خود قرار دارد و به­ترتیب با افزایش دمای هوا و میزان پوشش گیاهی، این شاخص رو به افزایش است. فاکتور L اهمیت به­سزایی در برآورد شاخص پوشش گیاهی تعدیل شده برحسب خاک و در نهایت، صحت­سنجی برآوردهای بدست آمده تبخیروتعرق برای منطقه مورد مطالعه دارد که به پوشش منطقه وابسته است. در این تحقیق برای منطقه مورد مطالعه مقدار L=0.6 تخمین زده شد که در مقایسه با دیگر مقادیر مورد بررسی، دارای کمترین مقدار خطا بود ((RMSE=0.6. الگوریتم SEBAL نسبت به سه الگوریتم دیگر به داده­های لایسیمتری نزدیکتر بوده و از دقت بالاتری برخوردار است. عملکرد مناسب الگوریتم SEBAL به دلیل جزئی‌نگری در فرمولاسیون و اجرای این الگوریتم بوده است. الگوریتم SSEB براساس تئوری ساده­تر و برمبنای انرژی حرارتی سطح زمین بوده که نسبت به الگوریتم SEBAL در رده دوم قرار می­گیرد. الگوریتم دومنبعی ضعیف­ترین نتایج را در میان الگوریتم‌ها از خود نشان داد. در مقایسه عملکرد تصاویر ماهواره­ای بطورکلی سنجنده MODIS به دلیل قدرت تفکیک زمانی مناسب و تعدد تصاویر نسبت به دو سنجنده ETM+ و TM و ارائه سری زمانی بیشتر، برای برآورد تبخیروتعرق در مقیاس منطقه­ای مناسب می­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of Single-source and Two-source Algorithms for Estimating Evapotranspiration in Borazjan

نویسندگان [English]

  • B. Bahmanabadi 1
  • A. Kaviani 2
1 Imam Khomeini International University, Qazvin
2 Imam Khomeini International University, Qazvin
چکیده [English]

Introduction: The exact estimation of evapotranspiration has significant importance in the programming of irrigation development and other distribution systems and water usage. Since the main user of water in the country is the agriculture sector, therefore, the exact estimation of plants’ water demand has been adverted extensively. The assessment methods of reference evapotranspiration are classified in two types of direct and indirect. The calculation of reference evapotranspiration in scientific and in vitro form and with high accuracy is possible by using lysimeter but in comparison to the indirect methods that are based on the climatic data of weather stations, the use of lysimeter is unfortunately inefficient. This is not just for the time consuming and high cost of lysimeter but it is for the limitation of weather stations and spottiness of the estimated values; in this way it is not possible to expand the obtained results to the large scale. Remote sensing is an authentic technique for the assessment of evapotranspiration in large scale which do not consume much time and money. The existence of different satellites by having different spatial and temporal resolution, redouble the importance and usability of this technique
Material and Methods: Actual evapotranspiration assessment in the region were done based on SEBAL, SSEB and TSEB algorithms on 46 imageries of MODIS, seven imageries of Landsat7 (ETM+) and seven imageries of Landsat5 (TM) in years of 2001-2003. Multiplicity of imageries of MODIS show the proper time resolution of this sensor and is a reason for less errors in the assessment of reference evapotranspiration. In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites.
Result and Discussion: In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites, MODIS shows the least errors (respectively, RMSE=0.856, 1.385 and 2.7 mm/day), then Landsat7 is placed in the second class by having higher spatial resolution (respectively, RMSE=1.042, 1.56 and 2.76 mm/day) and Landsat5 has the highest errors (respectively, RMSE = 1.14, 1.97 and 3.06 mm/day). NDVI was found at the lowest amount in the beginning of cultivation period because of germination and sparseness of vegetation, and increase respectively by increasing temperature and crop canopy. L factor has a significant importance in the assessment of SAVI which is related to the area crop coverage percentage. Amount of L has been estimated as L=0.6 that has the least errors in comparison to the others.
Conclusion: In this study, the proper amount for L factor in estimation of the SAVI amount was about 0.6 which was based on the investigations on soil correction factor, the results of statistical indexes and the type and dispersal of vegetation in the region. The accuracy estimation of evapotranspiration of two single-source algorithms of SEBAL and SSEB and one two-source algorithm of TSEB in Bushehr province were evaluated. SEBAL algorithm presented more exact results based on statistical indexes among two single-source algorithms and the obtained results in 95% level of this algorithm showed significant differences with lysimetric measurements. This algorithm was chosen as the premier algorithm in the region. Two-source algorithm of TSEB showed the highest amount of errors. Satellite imageries by having higher spatial resolution estimated evapotranspiration with higher accuracy, the reason of which is proper choosing of cold and hot pixels. Although, because of having proper time resolution and variation of image numbers and also presenting of more time series in comparison to ETM+ and TM, MODIS was more adverted. ETM+ which is located on Landsat satellite was lied in the second place because of its resolution and having higher spatial resolution.

کلیدواژه‌ها [English]

  • NDVI
  • SAVI
  • SEBS
  • SEBAL
  • TSEB
1- Abdoli H., Eslamian S.S., and Abedi Koohpaei J. 2011. The use of Landsat7 satellite images and MODIS for estimating evapotranspiration through remote sensing in irrigation management. 3rd Irrigation and Drainage Network Management National Conference (IDNC201). Faculty of Irrigation Engineering, Shahid Chamran University. (In Persian with English abstract)
2- Allen R.G., Raes L.S., and Smith M. 1998. Crop Evapotranspiration Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56, FAO, Rome, Italy. 301 p.
3- Allen R.G., Tasumi M., and Trezza R. 2007.Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. Journal of Irrigation and Drainage Engineering 133: 380–394.
4- Allen R.G., Tasumi M., Morse A.T., and Trezza R. 2005. A Landsat-based Energy Balance and Evapotranspiration Model in Western US Water Rights Regulation and Planning. Journal of Irrigation and Drainage Engineering 19: 251–268.
5- Asare Mostaghim M., Rahimi Khoob A., and Asare Mostaghim L. 2011. Using SSEB Algorithm to Analyze the Process of Changes in the Amir Kabir Cane Sugar Crop Field Using Remote Sensing Techniques. Second National Conference on Combating Desertification and Sustainable Development of Iran's Kawiri Lagoon. (In Persian)
6- Bala A., Rawat K.S., and Misra A.K. 2015. Assessment and Validation of Evapotranspiration Using SEBAL Algorithm and Lysimeter Data of IARI Agricultural Farm. Geocarto International 739-764.
7- Bastiaanssen W., Noordman E., Pelgrum H., Davids G., Thoreson B., and Allen R. 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
8- Bastiaanssen W.G.M., Menenti M., Feddes R.A., and Holtslag A.A.M. 1998. A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL), Journal of Hydrology 212-213: 198-212.
9- Bastiaanssen W.G.M. 2000. SEBAL-Based Sensible and Latent Heat Fluxes in the Irrigated Gediz Basin, Turkey. Journal of Hydrology 229: 87-100.
10- Bastiaanssen W.G.M. 2002. SEBAL- Surface Energy Balance Algorithms For Land (Advanced Training and User’s Manual). Funded by a NASA EOSDIS/Synergy grant from the Raytheon Company through The Idaho Department of Water Resources.
11- Betts A., Ball J., Beljaars A., Miller M., and Viterbo P.A. 1996. The land surfaceatmosphere interaction: a review based on observational and global modeling perspectives. Journal of Geophysical Research 101: 7209–7226
12- Bos M.G., Kselik R.A.L., Allen R.G., and Molde D.J. 2009. Water requirements for irrigation and the environment. Springer, Dordrecht, ISBN 978-1-4020-8947-3
13- Chow V.T., Maidment D.R., and Mays L.W. 1988. Applied Hydrology, illustrate. ed. Mc graw-Hill Higher Education, New York, NY.
14- Colaizzi P.D., Kustas W.P., Anderson M.C., Agam N., Tolk J.A., Evett S.R., Howell T.A., Gowda P.H., and O’Shaughnessy S.A. 2012a. Two-source energy balance model estimates of evapotranspiration using component and compositesurface temperatures, Adv. Water Resour., 50: 134–151, doi: 10.1016/j.advwatres.2012.06.004.
15- Dominique C., Bernard S., and Albert O. 2005. Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches. Irrigation and Drainage Systems 19: 223–249.
16- Gebremichael M., Wang J., and Sammis T.W. 2010. Dependence of Remote Sensing Evapotranspiration Algorithm on Spatial Resolution. Atmospheric Research 96: 489-495.
17- Gowda P., Senay G., Howell T., and Marek T.H. 2009.Lysimetric Evaluation of Simplified surface Energy Balance Approach in the TEXAS High Plains. Applied Engineering in Agriculture 25(5): 665-669.
18- Huete A.R., Post D.F., and Jackson R.D. 1984. Soil spectral effects and 4-space vegetation discrimination. Journal of Remote sensing of Environment 15: 155-165.
19- Huntingford C., Verhoef A., and Stewart J. 2000. Dual versus single source models for estimating surface temperature of African savannah. Journal of Hydrology and Earth System Sciences 4: 185-191.
20- Kustas W.P., and Norman J.M. 1997. A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations, Journal of Water Resources Research 33: 1495-1508.
21- Kustas W.P., and Norman J.M. 1999. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agricultural and Forest Meteorology 94: 13–25.
22- Kustas W.P., Anderson M.C., Cammalleri C., and Alfieri J.G. 2013. Utility of a Termalbase Two- source Energy Balance Model for Estimating Surface flux over Complex Landscapes. Procedia Environmental Science 19: 224-230.
23- Kustas W.P., and Norman J.M. 2000. A two-source energy balance approach usingdirectional radiometric temperature observations for sparse canopy coveredsurfaces. Agron. J. 92: 847–854
24- Li F., Kustas W.P., Prueger J.H., Neale C.M., and Jackson T.J. 2005. Utility of remote sensing-based two-source energy balance model under low-and high-vegetation cover conditions, Journal of Hydrometeorology 6: 878–891, doi:10.1175/JHM464.1,
25- Maeda E.E., Wiberg D.A., and Pellikka P.K.E. 2011. Estimating reference evapotranspiration using remote sensing and empirical models in a region with limited ground data availability in Kenya. Applied Geography 31: 251-258.
26- Mata M.D., Salunke K.A., and PBhangale P. 2014. Evaluation of Evapotranspiration. International Journal of Research in Engineering and Technology 3(9): 43-47
27- Mobasheri M.R., Khavarian H., and Moussaoui H. 2006. Error estimates of ET from Sensible Heat in the SEBAL.National Conference on Irrigation and Drainage network management, Shahid Chamran University, Department of Water Engineering. (In Persian with English abstract)
28- Mohseni Saravi M., Ahmadi H., and Nosrati K. 2010. Estimation of evapotranspiration in Taleghan Basin using SEBAL. The First International Conference on Plant, Water, Soil and Weather Modeling. International center for science, high technology, environmental sciences. Shahid Bahonar University of Kerman. (In Persian with English abstract )
29- Norman J.M., Kustas W.P., Humes K.S. 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Journal of Agricultural and Forest Meteorology 77: 263-293.
30- Nouri H., Faramarzi M., Sobhani B., and Sadeghi S.H. 2017. Estimation of Evapotranspiration based Surface Energy Balance Algorithm for Land (SEBAL) using landsat 8 and Modis images. Journal of Applied Ecology and Enviromental Research 15(4): 1971-1982.
31- Qi J., Chehbouni A., Huete A.R., and Kerr Y.H. 1994. Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sensing of Environment 48: 119-126.
32- Ruhoff A.L., Paz A.R., Collischonn W., Aragao L.E., Rocha H.R., and Malhi Y.S. 2012. A MODIS-based energy balance to estimate evapotranspiration for clear-sky days in Brazilian tropical savannas. Remote Sensing (Basel) 4: 703–725.
33- Sanaei Nejad S.H., Noori S., and Hasheminia S.M. 2011. Estimation of Evapotranspiration Using Satellite Image Data in Mashhad area. Journal of Water and Soil 25(3): 540-547. (In Persian with English Abstract)
34- Senay G.B., Budde M., Verdin J.P., and Melesse A. 2007. A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields. Journal of Sensors 7: 979-1000.
35- Senay GB,. et al. 2013. Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach. Journal of the American Water Resources Association 49(3): 577–591.
36- Senay M., Budde J., and Verdin. 2011. Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Journal of Agricultural Water Management 98: 606–618.
37- Senay G.B., Verdin J.P., Lietzow R., and Melesse A.M. 2008. Global daily reference evapotranspiration modeling and evaluation. Journal of the American Water Resources Association 44: 969–979.
38- Sentelhas PC., Gillespie TJ., and Santos EA. 2010. Evaluation of FAO Penman–Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada. Agricultural Water Management 97: 635–644.
39- Tang R.., Li Z.L., and Sun X. 2013. Temporal upscaling of instantaneous evapotranspiration: An intercomparison of four methods using eddy covariance measurements and MODIS data. Remote Sensing of Environment 138: 102–118.
40- Tasumi M., Trezza R., Allen R.G., and Wright J.L. 2005. Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid U.S. springer. Irrigation and Drainage System 19: 355-376.
41- Timmermans W.J., Kustas W.P., Anderson M.C., and 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: 369-384.
42- Valipour M. 2015. Investigation of Valiantzas’ evapotranspiration equation in Iran. Theoretical and Applied Climatology 121(1): 267–278.
43- Wenjing L. 2006: Satellite based Regional-Scale Evapotranspiration in the Hebi Plain, Northeastern China, MSc Thesis, Geo-Information science and Earth Observation, international institute for Geo-Information science and Earth Observation Enschede, the Netherland.