تخمین تولید محصول یونجه با استفاده از تصاویر ماهواره‌ای Sentinel-2 منطقه مورد مطالعه: شرکت کشاورزی و دام‌پروری مگسال (قزوین)

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناس ارشد سنجش از دور، مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران

2 استادیار مرکز مطالعات سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه شهید بهشتی

3 دانشجوی دکتری سنجش از دور، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران

4 دانشجوی دکتری سنجش از دور، دانشکده نقشه برداری (ژئودزی و ژئوماتیک)، دانشگاه صنعتی خواجه نصیرالدین طوسی

چکیده

در طول چند دهه گذشته، شاخص‌های پوشش‌گیاهی متعددی برای تخمین تولید محصولات کشاورزی توسعه داده ‌شده‌اند که هر یک از آن‌ها با توجه به باندهای مورد استفاده و فرمول جبری خود، به مقادیر متفاوتی از تراکم و شاخص سطح برگ گیاهان زراعی حساسیت دارند. مطالعه بعضی از محصولات زراعی چندساله مانند یونجه، که در هر سال به دفعات برداشت می‌شود، بسیار پیچیده بوده و کمتر مورد توجه قرار گرفته است. لذا در این مقاله، از مهم‌ترین شاخص‌های پوشش‌گیاهی توسعه داده‌ شده در برآورد تولید یونجه، توسط تصاویر سری زمانی Sentinel-2 استفاده می‌شود. در این تحقیق، اقدام به جمع‌آوری دوره‌ای 144 نمونه، به شیوه تخریبی از مزارع زیر­کشت محصول یونجه شرکت کشاورزی و دامپروری مگسال (قزوین)، به‌صورت تقریباً نزدیک به زمان گذر ماهواره، شد و سپس کارایی 10 شاخص از معروف‌ترین شاخص‌های پوشش‌گیاهی، مبتنی بر تصاویر Sentinel-2 برای تخمین تولید محصول یونجه، مورد ارزیابی قرار گرفت. نتایج تحقیق حاضر، نشان داد که تولید تخمین زده‌شده یونجه، با استفاده از شاخص  نسبت به سایر شاخص‌ها، دارای بالاترین همبستگی  و کمترین جذر میانگین مربعات  با داده‌های برداشت‌شده میدانی در اواسط مرداد ماه بوده است. به­علاوه در نتایج این تحقیق، نشان داده شد که شاخص‌های لبه قرمز، مشکل اشباع‌شدگی شاخص‌های پوشش‌گیاهی در محصول یونجه را نتوانسته‌اند برطرف کنند و شاخص­های پوشش گیاهی سبز، نسبت به شاخص‌های لبه قرمز جهت تخمین تولید این محصول، توانایی بیشتری را نشان داده‌اند.

کلیدواژه‌ها

عنوان مقاله [English]

Alfalfa yield estimation using Sentinel-2 satellite images- a case study in Magsal Agricultural and Production Company (Qazvin)

نویسندگان [English]

  • Farzaneh hadadi 1
  • Mohsen m_azadbakht 2
  • Maedeh Behifar 3
  • Hamid Salehi Shahrabi 3
  • amir moeinirad 4
1 Remote sensing expert, Iranian Space Research Center
2 Assistant Professor in Remote Sensing, Remote Sensing and GIS Research Center
3 PhD student in Remote Sensing, Remote Sensing and GIS department, School of Geography, University of Tehran
4 PhD student in Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology
چکیده [English]

Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, which are harvested several times annually, is very complicated and has received less attention. Therefore, in this paper, the most important vegetation indices developed to estimate alfalfa yield are using Sentinel-2 time series images. In this research, 144 alfalfa samples were collected periodically in a destructive way from alfalfa farms of Magsal Agricultural and Production Company (Qazvin) near the time of satellite pass, and then the efficiency of 10 of the most famous vegetation indices to estimate alfalfa yield was evaluated based on Sentinel-2 images. The results of this research showed that the estimated alfalfa yield using the  index had the highest correlation () and the lowest root-mean-square-error (RMSE = 0.316 ) compared to the field data collected in the middle of August. In addition, the results showed that the red edge indices did not solve the saturation problem of vegetation indices and that the green vegetation indices were more capable of estimating alfalfa yield than the red edge indices.

کلیدواژه‌ها [English]

  • remote sensing
  • Agriculture
  • Red edge Index
  • Yield Estimation
  • Alfalfa
  • Sentinel-2

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