نشریه آبیاری و زهکشی ایران

نشریه آبیاری و زهکشی ایران

ارزیابی مدل کارایی مصرف تابش در برآورد زیست توده و عملکرد گندم آبی بر اساس تصاویر ماهواره های لندست 8 و 9 (مطالعه موردی: حوضه دریاچه ارومیه)

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی اب دانشکده کشاورزی دانشگاه ارومیه
2 استاد گروه مهندسی آب، دانشگاه ارومیه، ارومیه، ایران
3 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه
چکیده
با توجه به اینکه روش‌های سنتی اندازه‌‌گیری زمینی نیاز به هزینه‌، زمان و نیروی انسانی زیاد دارند تمایل به استفاده از روش‌های غیرمستقیم مانند سنجش از دور افزایش یافته است. بنابراین هدف از این پژوهش محاسبه زیست‌توده خشک، شاخص برداشت و عملکرد دانه گندم آبی با استفاده از سنجش از دور در حوضه دریاچه ارومیه بود. برای این منظور حوضه دریاچه ارومیه به شش زیرحوضه تقسیم گردید و از مدل کارایی مصرف تابش (RUE) استفاده شد. در این پژوهش از 1115 مزرعه گندم آبی استفاده شد. نتایج پایش مزارع و شاخص NDVI نشان داد که جوانه‌زنی گندم آبی در سطح حوضه به طور متوسط از تاریخ 20 آبان ماه شروع شده و برداشت این محصول نهایتاً تا 30 تیرماه ادامه داشته است. مقدار شاخص‌های ضریب همبستگی (R)، ضریب نش-ساتکلیف (NSE)، ریشه میانگین مربعات خطای نرمال شده (nRMSE) و ضریب باقی‌مانده (CRM) در مرحله واسنجی به ترتیب برابر 90/0، 76/0، 15 درصد و 38/6 درصد و در مرحله صحت سنجی به ترتیب برابر 92/0، 72/0، 14 درصد و 21/7 درصد بدست آمد. بررسی شاخص برداشت (HI) نشان داد که میانگین این شاخص در زیرحوضه‌های مختلف بین 40/0 تا 45/0 است و به طور کلی میانگین این شاخص برای حوضه دریاچه ارومیه 42/0 بدست آمد. نتایج بررسی زیست‌توده و عملکرد دانه حاکی از تفاوت‌های چشم‌گیر در مقادیر آن‌ها در زیرحوضه‌های مختلف بود. بیشترین زیست‌توده و عملکرد دانه در زیرحوضه زرینه- سیمینه با میانگین 12098 و 5261 کیلوگرم بر هکتار و کمترین آن‌ها در زیرحوضه غرب- دریاچه با میانگین 8651 و 3723 کیلوگرم بر هکتار مشاهده گردید.
کلیدواژه‌ها

عنوان مقاله English

Assessment of Radiation Use Efficiency Model in Estimating Biomass and Yield of Irrigated Wheat Based on Landsat 8 and 9 Satellite Images (Case Study: the Urmia Lake Basin)

نویسندگان English

Fereshteh Nasimi 1
Javad Behmanesh 2
Vahid Rezaverdinejad 3
1 Department of water engineering, Urmia university
2 Professor of Water Engineering, Department of Water Engineering, Urmia University. Urmia. Iran
3 Department of Water Engineering, Urmia University, Iran
چکیده English

Given that conventional ground-based methods require significant costs, time, and manpower, there is an increasing inclination towards using indirect methods such as remote sensing. Therefore, the aim of this research was to calculate dry biomass, harvest index, and grain yield of irrigated wheat using remote sensing in the Urmia Lake Basin. For this purpose, the Urmia Lake Basin was divided into six sub-basins, and the Radiation Use Efficiency (RUE) model was utilized. In this study, 1115 irrigated wheat fields were used. Monitoring results of the fields and the NDVI index indicated that irrigated wheat germination in the basin began on average from November 11th, and harvesting of this crop continued until July 21th. The values of correlation coefficient (R), Nash-Sutcliffe coefficient (NSE), normalized root mean square error (nRMSE), and coefficient of residual mass (CRM) in the calibration step were 0.90, 0.76, 15%, and 6.38%, respectively, and in the validation step, they were 0.92, 0.72, 14%, and 7.21%, respectively. Examination of the harvest index (HI) indicated that the average of this index in different sub-basins ranged from 0.40% to 0.45%, with an overall average of 0.42% for the Urmia Lake Basin. The results of biomass and grain yield assessment revealed significant differences in their values among different sub-basins. The highest biomass and grain yield were observed in the Zarrineh-Simineh sub-basin with averages of 12098 and 5261 kilograms per hectare, respectively, while the lowest values were observed in the West-Lake sub-basin with averages of 8651 and 3723 kilograms per hectare, respectively.

کلیدواژه‌ها English

Water stress
Remote sensing
Harvest index
NDVI index
جمالی، ر.، بشارت، س.، یاسی، م. و امیرپوردیلمی، ا. 1397. ارزیابی راندمان­های آبیاری، کارآیی مصرف و بهره­وری آب در حوضه دریاچه ارومیه (مطالعه موردی شبکه آبیاری و زهکشی زرینه­رود). نشریه علوم آب و خاک. 22(3): 117-130.
خلیلی، ع.، بذرافشان، ج. و چراغعلی‌زاده، م. 1401. بررسی تطبیقی نقشه‌های اقلیمی ایران در طبقه‌بندی دمارتن گسترش داده شده و کاربست روش برای پهنه‌بندی اقلیم جهان. هواشناسی کشاورزی. 10(1): 3-16.
عُنّابی­میلانی، ا. و موسوی­منش، ش. 1397. تعیین لایسیمتری تبخیرتعرق و ضریب گیاهی گندم در دشت تبریز و مقایسه آن با روش پیشنهادی فائو 56. هواشناسی کشاورزی. 6(2): 44-57.
Allen, R., Tasumi, M. and Trezza, R. 2002. Advanced Training and Users Manual of Surface Energy Balance Algorithms for Land (SEBAL), Version 1.0. Idaho Implementation. August, 2002.
Awad, M.M. 2019. Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques. Agriculture. 9: 54. https://doi.org/10.3390/AGRICULTURE9030054
Barideh, R. and Nasimi, F. 2022. Investigating the changes in agricultural land use and actual evapotranspiration of the Urmia Lake basin based on FAO’s WaPOR database. Agricultural Water Management. 264: 107509. https://doi.org/10.1016/J.AGWAT.2022.107509
Barideh, R., Veysi, S., Ebrahimipak, N. and Davatgar, N. 2022. The challenge of reference evapotranspiration between the WaPOR data set and geostatistical methods. Irrigation and Drainage. 1–12. https://doi.org/10.1002/IRD.2738
Besharat, S., Barão, L. and Cruz, C. 2020. New strategies to overcome water limitation in cultivated maize: Results from sub-surface irrigation and silicon fertilization. Journal of Environmental Management. 263: 110398. https://doi.org/10.1016/J.JENVMAN.2020.110398
Chavez, J.C., Ganjegunte, G.K., Jeong, J., Rajan, N., Zapata, S.D., Ruiz-Alvarez, O. and Enciso, J. 2022. Radiation Use Efficiency and Agronomic Performance of Biomass Sorghum under Different Sowing Dates. Agronomy. 12(6). https://doi.org/10.3390/agronomy12061252
Duc, L. and Sawada, Y. 2023. A signal-processing-based interpretation of the Nash–Sutcliffe efficiency. Hydrology and Earth System Sciences. 27: 1827–1839.
Dwivedi, M., Saxena, S. and NeetuRay, S.S. 2019. Assessment of Rice Biomass Production and Yield Using Semi-Physical Approach and Remotely Sensed Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 423: 217–222. https://doi.org/10.5194/ISPRS-ARCHIVES-XLII-3-W6-217-2019
Fu, Y., Huang, J., Shen, Y., Liu, S., Huang, Y., Dong, J., Han, W., Ye, T., Zhao, W. and Yuan, W. 2021. A Satellite-Based Method for National Winter Wheat Yield Estimating in China. Remote Sensing. 13: 4680. https://doi.org/10.3390/RS13224680
Hassan, D.F., Abdalkadhum, A.J., Mohammed, R.J. and Shaban, A. 2022. Integration Remote Sensing and Meteorological Data to Monitoring Plant Phenology and Estimation Crop Coefficient and Evapotranspiration. Journal of Ecological Engineering. 23: 325–335. https://doi.org/10.12911/22998993/146267
Javadian, M., Behrangi, A., Gholizadeh, M. and Tajrishy, M. 2019. METRIC and WaPOR Estimates of Evapotranspiration over the Lake Urmia Basin: Comparative Analysis and Composite Assessment. Water. 11: 1647. https://doi.org/10.3390/W11081647
Jiang, L. and Islam, S. 1999. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophysical Research Letters. 26(17): 2773–2776. https://doi.org/10.1029/1999GL006049
Lobell, D.B., Asner, G.P., Ortiz-Monasterio, J.I. and Benning, T.L. 2003. Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems & Environment. 94: 205–220. https://doi.org/10.1016/S0167-8809(02)00021-X
Mahakosee, S., Jogloy, S., Vorasoot, N., Theerakulpisut, P., Holbrook, C.C., Kvien, C.K. and Banterng, P. 2022. Light Interception and Radiation Use Efficiency of Cassava under Irrigated and Rainfed Conditions and Seasonal Variations. Agriculture. 12: 725. https://doi.org/10.3390/AGRICULTURE12050725
Mokhtari, A., Noory, H., Balkhi, A. and Alaghmand, S. 2021. Comparison of Three Different Satellite-Based Approaches for Aboveground Biomass Estimation. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 89: 33–47. https://doi.org/10.1007/S41064-020-00134-9/TABLES/8
Mokhtari, A., Noory, H., Vazifedoust, M., Palouj, M., Bakhtiari, A., Barikani, E., Zabihi Afrooz, R.A., Fereydooni, F., Sadeghi Naeni, A., Pourshakouri, F., Badiehneshin, A. and Afrasiabian, Y. 2019. Evaluation of single crop coefficient curves derived from Landsat satellite images for major crops in Iran. Agricultural Water Management. 218: 234–249. https://doi.org/10.1016/J.AGWAT.2019.03.024
Monteith, J.L. and Moss, C.J. 1977. Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London. B, Biological Sciences. 281: 277–294. https://doi.org/10.1098/RSTB.1977.0140
Monteith, L.J. 1978. Reassessment of maximum growth rates for C3 and C4 Crops. Agriculture. 14: 1–5.
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. 50: 885−900.
Nash, J. E. and Sutcliffe, J. V. 1970. River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3): 282–290.
Priestley, C.H.B. and Taylor, R.J. 1972. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review. 100(2): 81–92. https://doi.org/https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
Rinaldi, M. and Garofalo, P. 2011. Radiation-use efficiency of irrigated biomass sorghum in a Mediterranean environment. Crop and Pasture Science. 62(10): 830–839. https://doi.org/10.1071/CP11091
Saha, S., Banerjee, S., Mondal, S., Mukherjee, A., Nath, R. and Chowdhury, S. 2022. Evaluating radiation interception pattern and RUE of green gram grown in Lower Gangetic Plains and assessing future yield based on RUE. Journal of Agrometeorology. 24: 3–9. https://doi.org/10.54386/JAM.V24I1.830
Servia, H., Pareeth, S., Michailovsky, C.I., de Fraiture, C. and Karimi, P. 2022. Operational framework to predict field level crop biomass using remote sensing and data driven models. International Journal of Applied Earth Observation and Geoinformation. 108: 102725. https://doi.org/10.1016/J.JAG.2022.102725
Shi, D., Huang, Q., Liu, Z., Liu, T., Su, Z., Guo, S., Bai, F., Sun, S., Lin, X., Li, T. and Yang, X. 2022. Radiation use efficiency and biomass production of maize under optimal growth conditions in Northeast China. Science of The Total Environment. 836: 155574. https://doi.org/10.1016/J.SCITOTENV.2022.155574
Tasumi, M. 2019. Estimating evapotranspiration using METRIC model and Landsat data for better understandings of regional hydrology in the western Urmia Lake Basin. Agricultural Water Management. 226: 105805. https://doi.org/10.1016/J.AGWAT.2019.105805
Tripathi, A.M., Pohanková, E., Fischer, M., Orság, M., Trnka, M., Klem, K. and Marek, M.V. 2018. The Evaluation of Radiation Use Efficiency and Leaf Area Index Development for the Estimation of Biomass Accumulation in Short Rotation Poplar and Annual Field Crops. Forests. 9(4): 168. https://doi.org/10.3390/F9040168
Venancio, L.P., Eugenio, F.C., Filgueiras, R., Da Cunha, F.F., Dos Santos, R.A., Ribeiro, W.R. and Mantovani, E.C. 2020. Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application. PLOS ONE. 15: e0235620. https://doi.org/10.1371/JOURNAL.PONE.0235620
Zheng, Y., Zhang, M., Zhang, X., Zeng, H. and Wu, B. 2016. Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products. Remote Sensing. 8: 824. https://doi.org/10.3390/RS8100824