شبیه سازی عملکرد و اجزای عملکرد گیاه برنج با استفاده از مدل SWAP و فناوری سنجش از دور به‌منظور استفاده بهینه از منابع آب و خاک

نوع مقاله: مقاله اصلی

نویسندگان

1 دانشجوی 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 استادیار، گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران

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

10.22034/jewe.2020.242119.1398

چکیده

با توجه به اهمیت منابع آب‌وخاک در توسعه کشاورزی پایدار، افزایش جمعیت جهان و نیاز روزافزون به تولیدات زراعی، پیش‌بینی عملکرد محصول با استفاده از مدل­های شبیه­ساز گیاهی و فناوری سنجش از دور بسیار با اهمیت است. پژوهش حاضر با هدف برآورد عملکرد اجزای برنج شامل کاه، شلتوک و زیست­توده رقم هاشمی طی مراحل مختلف رشد با مدل SWAP و ارائه معادلات رگرسیونی با استخراج شاخص­های گیاهی NDVI و SAVI از تصاویر ماهواره­ای سنتینل 2 و لندست 7 و 8 در مؤسسه تحقیقات برنج کشور انجام شد. مقایسه متغیرهای آماری عملکرد گیاه برنج نشان داد که میانگین مقادیر ضریب تبیین (R2) و شاخص کارایی مدل­ (EF) در برآورد عملکرد اجزای برنج در مراحل مختلف رشد با مدل SWAP به­ترتیب بیش­تر از 7/0 و 9/0 و دارای خطای 93/1 تا 54/6% معادل 21/134 الی kg/ha 43/470 بود. اختلاف اندک ‌بین مقادیر اندازه‌گیری‌شده و شبیه‌سازی‌شده نشان داد که مدل SWAP عملکرد برنج در منطقه موردمطالعه را با دقتی مناسب برآورد می‌کند. نتایج همچنین نشان داد که شاخص‌‌‌های NDVI و SAVI استخراج‌شده با دقتی بسیار خوب عملکرد اجزای برنج در مراحل مختلف رشد را برآورد می‌کنند. لیکن بیش­ترین مقدار همبستگی مربوط به مرحله رشد زایشی بود. در نهایت، R2 برای شاخص NDVI در مراحل مختلف رشد و نیز در کل دوره رشد برای کاه، شلتوک و زیست‌توده نسبت به شاخص SAVI بیش­تر بوده و شاخص NDVI از دقت بیش­تری برخوردار بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Simulation of Rice Yield and its Components Using SWAP Model and Remote Sensing Technology for Optimal Use of Water and Soil

نویسندگان [English]

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

Given the importance of soil and water resources in the development of sustainable agriculture, increasing world population and the growing need for crop production, predicting crop yields using plant simulation models and remote sensing technology is very crucial. The aim of this study was to estimate the yield of rice components including straw, paddy and biomass of Hashemi cultivar during different growth stages with SWAP model and to provide regression equations by extracting NDVI and SAVI plant indices from Sentinel-2 and Landsat-7 and 8 satellite images. It was done in the National Rice Research Institute. Comparison of statistical variables indicated that the mean values of coefficient of determination (R2) and model efficiency factor (EF) in estimating the yield of rice components in different stages of growth with SWAP model were more than 0.70 and 0.90, respectively, and with an error of 1.93 to 6.54% was equivalent to 134.21 to 470.43 kg/ha. The slight difference between the measured and simulated values showed that the SWAP model estimates the rice yield in the study area with appropriate accuracy. The results also showed that the extracted NDVI and SAVI indices with very good accuracy estimate the yield of rice components at different stages of growth. However, the highest amount of correlation was related to the reproductive development stage. Finally, R2 for NDVI at different growth stages as well as  the entire growth period for straw, paddy, and biomass were higher than the SAVI index, revealing more accuracy of NDVI than SAVI.

کلیدواژه‌ها [English]

  • Biomass
  • rice
  • satellite images
  • SWAP model
  • Vegetation indices
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