بهبود برآورد عملکرد محصول در مدل شبیه‌سازی SWAP با استفاده از داده‌های ماهواره‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد آبیاری و زهکشی گروه آبیاری و آبادانی پردیس کشاورزی و منابع طبیعی دانشگاه تهران

2 استادیار گروه آبیاری و آبادانی پردیس کشاورزی و منابع طبیعی دانشگاه تهران

3 استادیار گروه مهندسی آب دانشگاه گیلان

چکیده

به منظور برآورد عملکرد گیاهان در سطوح وسیع، از روش به‌روزرسانی مدل‌های رشد گیاه با داده‌های ماهواره‌ای استفاده می‌شود. هدف این تحقیق تعیین میزان بهبود برآورد عملکرد محصول در مدل شبیه‌سازی SWAP با استفاده از این روش بود. این تحقیق در سال زراعی 1390ـ 1391 در سه سامانة آبیاری عقربه‌ای، واقع در شبکة آبیاری قزوین، تحت کشت گیاهان ذرت علوفه‌ای و چغندرقند، انجام شد. اجرای مدل SWAP در دو مرحلة بدون به‌روزرسانی و با به‌روزرسانی با شاخص سطح برگ ماهواره‌ای انجام شد. برآورد عملکرد محصول چغندرقند و ذرت با مدل SWAP به‌روزرسانی‌شده به‌ترتیب 7/13 و 5/14 درصد در مقدار درصد خطا و 321/3 و 621/1 تن بر هکتار در مقدار RMSE بهبود یافت. نتایج به‌دست‌آمده نشان داد با به‌روزرسانی شاخص سطح برگ ماهواره‌ای می‌توان خطاهای داده‌های ورودی مدل و عدم ‌قطعیت موجود در آن‌ها را به میزان زیادی کاهش داد و با دقت مطلوبی عملکرد را در سطح وسیع و با تفکیک مزرعه به مزرعه برآورد کرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Improving Crop Yield Estimation through SWAP Model Using Satellite Data

نویسندگان [English]

  • Alireza Badiehneshin 1
  • Hamideh Noory 2
  • Majid Vazifedoust 3
1 Master science of irrigation and drainage , Department of Irrigation and Reclamation Eng., College of Agriculture and Natural Resources, University of Tehran
2 Assistance professor, Department of Irrigation and Reclamation Eng., College of Agriculture and Natural Resources, University of Tehran
3 Assistance professor, Water Engineering Department, University of Guilan
چکیده [English]

Updating the satellite data in crop growth models is a useful technique to estimate the crop yield on some large scale farms. Throughout the present study, the improvement of SWAP model simulation was investigated using this technique for crop yield estimation. The study was conducted during 2012 growing season at some three center- pivot farms, covered by fodder maize and sugar beet as the main crops in Qazvin Irrigation Network. SWAP model was run by two methods, namely through either non-updated or updated satellite Leaf Area Index (LAI) data. Results indicated that sugar beet and fodder maize yields estimated through updated SWAP model were improved by about 13.7 and 14.5 (%) in absolute percent error while they amounted to 3.321 and 1.621 (ton/ha) in RMSE.
The obtained results indicated that the LAI assimilation technique (using satellite data) can greatly reduce errors related to model input parameters. It can reduce the uncertainty as well as estimate fairly accurately the crop yield in a large area and on any individual farm.

کلیدواژه‌ها [English]

  • Fodder maize
  • leaf area index
  • remote sensing
  • Sugar beet
  • SWAP model
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