برآورد تبخیر-تعرق و ضریب گیاهی برنج با استفاده از مدل SWAP با و بدون تلفیق تصاویر ماهواره‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد، گروه علوم خاک، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران.

2 استادیار، گروه علوم خاک، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران

3 دانشیار گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران

4 موسسه تحقیقات برنج کشور . سازمان تحقیقات، آموزش و ترویج کشاورزی، رشت ، ایران

چکیده

با افزایش جمعیت، نیاز روز افزون جامعه به غذا و کاهش بازده آبیاری در مزارع، استفاده بهینه از منابع خاک و آب حائز اهمیت است. با گسترش فناوری سنجش از دور، دسترسی به اطلاعات از منابع زمینی به‌گونه‌ای گسترده و سریع فراهم شده است. پژوهش حاضر با هدف شبیه‌سازی تبخیر-تعرق و ضریب گیاهی برنج رقم هاشمی اصلاح شده طی مراحل مختلف رشد با استفاده از مدل SWAP و تصاویر ماهواره‌ای و مقایسه کارآیی این روش‌ها با یکدیگر در مؤسسه تحقیقات برنج کشور واقع در شهر رشت در سال زراعی 1396 انجام شد. بر پایه نتایج مجموع تبخیر-تعرق اندازه‌گیری شده با لایسیمتر، و شبیه‌سازی شده با مدل SWAP با و بدون بروزرسانی با داده‌های ماهواره‌ای به ترتیب 4/395، 2/373 و 6/363 میلی‌متر بود. میانگین ضریب گیاهی محاسبه شده در دوره‌های رشد رویشی، زایشی و رسیدگی به‌ ترتیب 13/1، 49/1، 21/1 به‌دست آمد. این ضرایب برای حالت شبیه‌سازی شده بدون بروزرسانی به‌ترتیب 02/1، 39/1، 04/1 و با برزورسانی داده‌های ماهواره‌ای به‌ترتیب 05/1، 43/1 و 07/1 به‌دست آمد. در نهایت، بر اساس آماره‌های محاسبه شده مدل SWAP در برآورد ضریب گیاهی (63/0=R2، 96/0=EF، 53/0=RMSE) و تبخیر-تعرق برنج (74/0=R2، 98/0=EF، 89/0=RMSE) از دقتی مناسب برخوردار بوده، لیکن با اندک اختلافی مدل SWAP بروزرسانی شده با داده‌های ماهواره‌ای در برآورد ضریب گیاهی (74/0=R2، 99/0=EF، 40/0=RMSE) و تبخیر-تعرق (86/0=R2، 99/0=EF، 75/0=RMSE) بهتر عمل کرده و می‌توان از تصاویر ماهواره‌ای به‌منظور بهبود کارایی مدل در برآورد تبخیر-تعرق و ضریب گیاهی برنج استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of the performance of SWAP model updated with satellite data in estimating of rice evapotranspiration and its crop coefficients

نویسندگان [English]

  • Hosein Pandi 1
  • Safoora Asadi Kapourchal 2
  • Majid Vazifedoust 3
  • Mojtaba Rezaei 4
1 Graduate M.Sc., Department of Soil Science, Faculty of Agricultural Sciences, University Of Guilan, Rasht, Iran.
2 Assistant Professor, Department of Soil Science, Faculty of Agricultural Sciences, University Of Guilan, Rasht, Iran
3 Associate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University Of Guilan, Rasht, Iran
4 Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran.
چکیده [English]

Due to the upcoming climate threats, challenge of water shortage and its impact on the food security of the growing population in Iran, the optimal use of soil and water resources is very important. With the development of remote sensing technologies, free access to a variety of field data has become widely available, which can be used to reduce the uncertainty of simulation models. The aim of this study was to simulate actual evapotranspiration (ETa) and rice crop coefficients (Kc) during its growth stages using the SWAP model updated with satellite data and evaluate the accuracy of the results with/without updating. This research was conducted at the National Rice Research Institute of Iran in Rasht in the year of 2017. Based on the obtained results, total ETa measured by lysimeter and simulated by SWAP model with and without updating were 395.4, 373.2 and 363.6 mm, respectively. The average crop coefficients during the growth stages of vegetative, reproductive and ripening were estimated as 1.13, 1.49, 1.21, respectively. The crop coefficients for the proposed stages estimated by SWAP model without using satellite data were 1.02, 1.39, 1.04, respectively. After updating with satellite data, the crop coefficients were modified as 1.05, 1.43 and 1.07, respectively. Finally, the statistical analysis indicated that the SWAP model has a reasonable performance in estimation of ETa (RMSE=0.89; EF=0.98; R2=0.74) and rice crop coefficients (RMSE=0.53; EF=0.96; R2=0.63). The results indicate that the SWAP model combined with satellite data improved the accuracy of ETa estimation (RMSE=0.75; EF=0.99; R2=0.86) and rice crop coefficient (RMSE=0.40; EF=0.99; R2=0.74) at field scale.

کلیدواژه‌ها [English]

  • Crop coefficient
  • remote sensing
  • rice
  • simulation
  • SWAP

Evaluation of the performance of SWAP model updated with satellite data in estimating rice evapotranspiration and its crop coefficients

 

EXTENDED ABSTRACT

 

Introduction

Determining evapotranspiration during the growing season is very important for estimating water consumption and crop production. Both the remote sensing and crop growth simulation models provide a variety of field data including actual evapotranspiration (ETa) and crop biomass production. The water requirement of rice has direct relationship with the quantity of ETa which is depends on the weather conditions, soil texture, crop growth period and farm management. Considering the variability of rice evapotranspiration in different climatic conditions and the importance of rice cultivation in Guilan province, this study was conducted to determine the crop coefficient of rice (modified Hashemi variety) during the different growth stages, to measure ETa by direct method (lysimeter), to simulate ETa with and without updating the SWAP model with satellite data, and to analyze the accuracy of outcomes.

Material and Methods

This research was conducted in a paddy field located at the Rice Research Institute of Iran in Rasht in 2017. The field is located at 37°12 N, 49°38 E, with an altitude of 24 m below the sea level. The rice variety used in this study was the modified Hashemi with growth period of 83 days. In order to directly determine the rice evapotranspiration, mini lysimeters with 75 cm diameter and 40 cm height were used. The experimental lysimeters were installed in the center of the farms with 20 m distance from each other and then filled with paddy soil. A number of 7 groups of rice seedlings were planted in each lysimeter based on the dimensions and spacing of the seedlings in the field. The required input data for SWAP model including soil data, irrigation, plant parameters and meteorological data was set from related sources. In the next step, Sentinel2 satellite images with a time step of 5 days and Landsat 7 and 8 were used to calculate the Normalized Difference Vegetation Index (NDVI) during the rice growth period. Then, the crop coefficients were derived using the calibrated NDVI-based equations recommended for rice crop coefficients in the similar climate. In order to evaluate the accuracy and efficiency of the results with and without updating, statistical indices including coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE), model efficiency (EF) and residual mass coefficient (CRM) were used.

Results and Discussion

Based on the obtained results, total ETa measured by the lysimeter and simulated by SWAP model with and without updating were 395.4, 373.2 and 363.6 mm, respectively. The average crop coefficients during the growth stages of vegetative, reproductive and ripening were estimated as 1.13, 1.49, 1.21, respectively. The crop coefficients for the proposed stages estimated by SWAP model without using satellite data were 1.02, 1.39, 1.04, respectively. After updating with satellite data, the crop coefficients were modified as 1.05, 1.43 and 1.07, respectively. Finally, the statistical analysis indicated that the SWAP model has a reasonable performance in estimation of ETa (RMSE=0.89; EF=0.98; R2=0.74) and rice crop coefficients (RMSE=0.53; EF=0.96; R2=0.63). The results indicate that the SWAP model combined with satellite data improved the accuracy of ETa estimation (RMSE=0.75; EF=0.99; R2=0.86) and rice crop coefficient (RMSE=0.40; EF=0.99; R2=0.74) at field scale.

Conclusion

The integration of remote sensing with SWAP model has improved the accuracy of simulation. In the current situation with upcoming water crisis, it is necessary to improve the water efficiency in consumption of limited water resources. Since the rainfall in the rice growing season is not sufficient to meet the rice water requirements, the irrigation authorities can use the results of this research to calculate the irrigation water requirements with higher accuracy and allocate the limited water resources with more efficiently.

 

Abdi, A., Asadi Kapourchal, S., Vazifedoust, M. & Rezaei, M. (2022). A novel satellite-based methodology for retrieving specific leaf area of rice (Hashemi cultivar) at field scale. Environmental Engineering and Management Journal, 21(12), 2093-2102.
Abdi, A., Asadi Kapourchal, S., Vazifedoust, M. & Rezaei, M. (2021). Investigation the Effect of Observed and Estimated Dry Matter from Satellite Imagery on the Accuracy of Hashemi Rice Yield Simulation Using SWAP Model. Water Management in Agriculture, 7(2), 103-118. (In Persian)
Abdi, A., Asadi Kapourchal, S., Vazifedoust, M., Rezaei, M. & Egdernejad, A. (2021). Capability of Updated SWAP Model with Satellite Images and AquaCrop Model in Simulating the Hashemi Rice Yield in Guilan Province. Water Management in Agriculture, 8(1), 89-102. (In Persian)
Allen, R.G., Pereira, L.S., Raes, D & Martın, M. (1998). Crop evapotranspiration.Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56, FAO, Rome, 300 pp.
Amiri, E. (2017). Evaluation of water schemes for maize under arid area in Iran using the SWAP Model. Communications in Soil Science and Plant Analysis, 48(16), 1963-1976.‏
Barideh, R., Besharat, S. & Khodaverdiloo, H. (2021). Derivation and Validation of Parametric Pedotransfer Functions of Soil Water Infiltration in Different Regions. Iranian Journal of Irrigation and Drainage, 15(4), 769-779. (In Persian).
Barlow, K., Christy, B., O’leary, G., Riffkin, P. & Nuttall, J. (2015). Simulating the impact of extreme heat and frost events on wheat crop production: A review. Field Crops Research, 171: 109-119.
Blanka, V., Ladányi, Z., Szilassi, P., Sipos, G., Rácz, A., & Szatmári, J. (2017). Public perception on hydro-climatic extremes and water management related to environmental exposure, SE Hungary. Water Resources Management, 31(5), 1619-1634.
Choudhury, B.U. & Singh, A.K. (2016). Estimation of crop coefficient of irrigatedtransplanted puddled rice by field scale water balance in the semi-aridIndo-gangetic Plains, India. Agricultural Water Management, 176, 142–150.
Dehghan, H., Alizadeh, A. & Haghayeghi, S.A. (2011). Water Balance Components Estimating in Farm Scale Using Simulation Model SWAP (Case Study: Neyshabur Region). Journal of Water and Soil, 24(6), 1265-1275. (In Persian)
Dong, Q., Zhan, C., Wang, H., Wang, F. & Zhu, M. (2016). A review on evapotranspiration data assimilation based on hydrological models. Journal of Geographical Sciences, 26(2), 230-242.
Doorenbos, J & Pruitt, W.O. (1977). Crop Water Requirements. FAO Irrigation and Drainage Paper No 24. Rome, Italy. 144 p.
FAO, (2018). FAOSTAT Database Collections. Food and Agriculture Organization of the United Nations. Food outlook biannual report on global food markets, Rome. Nov. 2018. URL: http://www.fao.org/faostat.
Farsadnia, F., Zahmati, S., Ghahremani, B. & Moghaddam Nia, A. (2016). Using Unsupervised Estimator Technique to Predict Reference Crop Evapotranspiration. Iran-Water Resources Research, 11(3), 31-42. (In Persian)
Frantar,P., Dolinar, M. & Kurnik, B. (2006). GIS based water balance of Slovenia, environmental agency of the republic of Slovenia. Geophysical Research Abstracts, pp 8-13.
Gee, G.W. & Bauder, J.W. (1986). Particle size analysis. In: Klute A (Ed.), Methods of soil analysis. Part 1. Physical and mineralogical methods, Agron, 2nd (ed.), Madison, WI, pp 404–408.
Ghamarniya, H., Jafarizadeh, M., Miri, E. & Gobadi, M. (2011). Coriandrum sativum L. crop coefficient determination in a semi-arid climate, Journal of Water and Irrigation Management, 25(2): 73–83.
Gholami Sefidkouhi, M.A., Bagheri khalili, Z. & ghalenovi, A. (2021). Investigation of Rice Actual Evapotranspiration and Crop Coefficients for Shiroudi and Hashemi Cultivars in Sari. Journal of Water Research in Agriculture (Soil and Water Sci.), 34(4), 505-516. (In Persian).
Grossman, R. and Reinsch, B.T.G. (2002). Bulk Density. In: J.H. Dane and G.C. Topp, Methods of soil analysis. Physical methods, Soil science society of America, Inc, Madison, Wisconsin, USA, Part 4.
Güçlü, Y. S., Subyani, A. M. & Şen, Z. (2017). Regional fuzzy chain model for evapotranspiration estimation. Journal of hydrology, 544, 233-241.
Han, C., Zhang, B., Chen, H., Wei, Z. & Liu, Y. (2019). Spatially distributed crop model based on remote sensing. Agricultural Water Management, 218, 165-173.
Irmak, A. & Kamble, B. (2009). Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation. Irrigation science, 28(1), 101-112.‏
Irmak, S., Haman, D.Z., & Jones, J.W. (2002). Valuation of Class Pans Coefficients for Estimating Reference Evapotranspiration in Humid Location. Journal of Irrigation and Drainage Engineering, 128(3), 153-159.
Jafari Sayadi, F., Gholami Sefidhouhi, M.A. & Ziatabar Ahmadi M. (2018). Leaf area index and crop coefficient estimation from operational land imager (OLI) sensor data. Iranian Journal of Water Research in Agriculture (Formerly soil and Water Sciences), 32(3), 395-404. (In Persian)
Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141-152.
Kamble, B & Irmak, A. (2008). Assimilating remote sensing-based ET into SWAP model for improved estimation of hydrological predictions. IGARSS 2008 IEEE International Geoscience and Remote Sensing Sympo ium 3: 1036–1039.
Kroes, J. G., & Van Dam, J. C. (2003). Reference manual SWAP version 3.03, Alterra Green World Research, Alterra Report 773, Wageningen University and Research Centre, Wageningen, The Netherlands.
Kroes, J.G., Van Dam, J.C., Groenendijk, P., Hendriks, R.F.A. & Jacobs, C.M.J. (2008). SWAP version 3.2. Theory description and user manual, Alterra, Wageningen university.
Kumari, A., Upadhyaya, A., Jeet , P., Al-Ansari, N., Rajput, J., Sundaram, P.K., Saurabh, K., Prakash, V., Singh, A. K., Raman, R. K., Gaddikeri, V. & Kuriqi, A. (2022). Estimation of actual evapotranspiration and crop coefficient of transplanted puddled rice using a modified non-weighing paddy lysimeter. Agronomy, 12, 1-20.
Kunnath-Poovakka, A., Ryu, D., Renzullo, L. J., & George, B. (2016). The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction. Journal of Hydrology, 535, 509-524.
Li, Z. L., Tang, R., Wan, Z., Bi, Y., Zhou, C., Tang, B., Yan, G & Zhang, X. (2009). A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9(5), 3801-3853.
Liang, S. & Qin, J. (2008). Data assimilation methods for land surface variable estimation. Advances in Land Remote Sensing, 313–339.
Losgedaragh, S. Z. & 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.
Lv, Y., Xu, J., Yang, S., Liu, X., Zhang, J. & Wang, Y. (2018). Inter-seasonal and cross-treatment variability in single-crop coefficients for rice evapotranspiration estimation and their validation under drying-wetting cycle conditions. Agricultural Water Management, 196, 154-161.‏
Ma, Y., Feng, S., Huo, Z. & Song, X. (2011). Application of the SWAP model to simulate the field water cycle under deficit irrigation in Beijing, China. Mathematical and Computer Modelling, 54 (3–4):1044–52.
Majnooni Heris, A., Nazemi, A.H., Sadraddini, A.A., Neyshapouri, M.R. & Shakiba, M.R. (2015). Determination of evapotranspiration, crop coefficient and growth stages of Canola by lysimetric data. Water and Soil Science, 25(1), 153-163. (In Persian)
Marin, F., Jones, J.W. & Boote, K.J. (2017). A stochastic method for crop models: including uncertainty in a sugarcane model. Agronomy Journal, 109(2): 483–495.
Ministry of Jahad in Agriculture (2021). Agricultural statistics. Tehran: Ministry of Jahad in Agriculture. Planning and Economical Division. Bureau for Statistics and Information Technology. (In Persian)
Modabberi, H., Mirlatifi, M. & Gholami M.A. (2014). Determination of evapotranspiration and crop coefficient of two rice cultivars in Mordab Plain (Guilan Province). Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources), 18 (67), 95-106. (In Persian)
Page, A. L., Miller, R. H. & Keeney, D. R. (Ed., 1982): Methods of soil analysis; 2. Chemical and microbiological properties, 2. Aufl. 1184 S., American Soc. of Agronomy (Publ.), Madison, Wisconsin, USA.
Pirmoradian, N., Kamgar-Haghighi, A.A. & Sepaskhah, A.R. (2002). Crop coefficient and water requirement of rice in Kooshkak region, Fars province. Journal of Agricultural Science and Natural Resources 6 (3): 15-23. (In Persian)
Pouryazdankhah, H., Razavipour, T., Khaledian, M.R. & Rezaei, M. (2014). Determining crop coefficient of Binam and Khazar cultivars of rice by lysimeter and controlled basins in Rasht region. Journal of Agroecology, 6(2), 238-249. (In Persian)
Rahimikhoob, A. (2016). Comparison ofM5 model tree and artificial neural network’s methodologies in modelling daily reference evapotranspiration from NOAA satellite images. Water Resources Management, 30, 3063–3075.
Ramezani Khojeen, A., Kheirkhah Zarkesh, M. M., Daneshkar Arasteh, P., Moridi, A. & Ali Mohammadi R. (2016). Sensitivity analysis of calculated evapotranspiration using daily energy balance model and comparing it with SEBAL model. Iran-Water Resources Research 12(1), 18–28. (In Persian)
Rostami, A. & Raeini-Sarjaz, M. (2016). Remotely sensed measurements of apple orchard actual evapotranspiration and plant coefficient using MODIS images and SEBAL algorithm (Case study: Ahar plain, Iran). Journal of Agricultural Meteorology, 4(1), 32-43. (In Persian)
Razavipor, T & M.R. Yazdani. (1999). Coefficient and Coefficient of Rice Basin Region of Gilan (Rasht), Sixth Congress of Soil Science, Ferdowsi University of Mashhad, Facultyof Agriculture, pp 621-692.
Singh U.K., Ren L., & Kang S. (2010). Simulation of soil water in space and time using an agrohydrological model and remote sensing techniques, Agricultural Water Management, 97 (8), 1210-1220.
Tang, H. & Li, Z. L. (2014). Estimation and validation of evapotranspiration from thermal infrared remote sensing data. In: Quantitative Remote Sensing in Thermal Infrared. Berlin and Heidelberg: Springer, 145–201.
Tomar, V.S. & O’Toole, J.C. (1980). Water use in lowland rice cultivation in asia: a review of evapotraspiration. Agricultural Water Management, 3: 83-106.
Tyagi, N.K., Sharma, D.K. & luthra, S.K. (2000). Determination of evapotranspiration and crop coefficients ofrice and sunflower with lysimeter. Agriculture Water Management, 45: 41-54.
van Lier, Q. J., Wendroth, O., & van Dam, J. C. (2015). Prediction of winter wheat yield with the SWAP model using pedotransfer functions: An evaluation of sensitivity, parameterization and prediction accuracy. Agricultural Water Management, 154, 29-42.
Vazifedoust M., Van Dam J.C., Feddes R.A., & Feizi M. (2008). Increasing water productivity of irrigated crops under limited water supply at field scale. Agricultural Water Management, 95: 89-102.
Vazifedoust, M., Van Dam, J.C., Bastiaanssen, W.G.M and Feddes, R.A. (2009). Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. International Journal of Remote Sensing, 30(10), 2523-2545.‏
Vu, S. H., Watanabe, H., & Takagi, K. (2005). Application of FAO-56 for evaluating evapotranspiration in simulation of pollutant runoff from paddy rice field in Japan. Agricultural Water Management, 76(3), 195-210.
Walkly, A & Black, J.A. (1934). An examination of digestion method for determiningsoil organic matter and proposed modification of the chromic acid titration. Soil Science, 37, 29-38.
Xu, X., Li, J. & Tolson, B.A. (2014). Progress in integrating remote sensing data and hydrologic modeling. Progress in Physical Geography, 38(4), 464-498.‏
Yaghoobzadeh, M., Boroomand Nasab, S., Izadpanah, Z. & Seyyed Kaboli, H. (2016). Estimation of Actual Evapotranspiration Using an Agro-Hydrological Model and Remote Sensing Techniques. Journal of Water and Soil, 30(4), 997-1008. (In Persian)
Yang, X., Bouman, B.A.M., Zhang, Q., Xue, C., Zhang, T., Xu, J. & Wangm, H. (2006). Crop Coefficient of Aerobic Rice in North China. Transactions of the Chinese Society of Agricultural Engineering, 22(2): 37-41.
Yin, J., Zhan, C., Wang, H. & Wang, F. (2017). Integration of remote sensing evapotranspiration (ET) model and hydrologic model for mapping daily ET time series at river basin scale. Hydrology Research, 48(2), 311-325.‏
Yoo, S.H., Choi, J.H. & Jang, M.W. (2008). Estimation of design water requirement using FAO Penman–Monteith and optimal probability distribution function in South Korea. Agricultural Water Management, 95: 845–853.
Zheng, J., Fan, J., Zhang, F., Yan, S., Guo, J., Chen, D. & Li, Z. (2018). Mulching mode and planting density affect canopy interception loss of rainfall and water use efficiency of dryland maize on the Loess Plateau of China. Journal of Arid Land, 5, 794–808.
Zhou, Y., Zhang, Y., Vaze, J., Lane, P. & Xu, S. (2013). Improving runoff estimates using remote sensing vegetation data for bushfire impacted catchments. Agricultural and Forest Meteorology, 182–183, 332–341.