• الصفحة الرئيسية
  • یک مدل فیزیکی مقیاس پذیر مبتنی بر سنجش از دور در برآورد عملکرد محصول مزارع برنج

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عنوان URL للمقالة


رقم المقالة : JSDE-2211-1248 (R1) زيارة : 95 الصفحة: 83 - 106

نوع المخطوط: ابحاث

یک مدل فیزیکی مقیاس پذیر مبتنی بر سنجش از دور در برآورد عملکرد محصول مزارع برنج

الموضوعات :

احسان آسمار 1 (دانشجوی دکتری گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.)
محمد حسن وحید نیا 2 (استادیار گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران. *(مسوول مکاتبات))
مجتبی رضایی 3 (استادیار موسسه تحقیقات برنج کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، رشت، ایران.)
ابراهیم امیری 4 (استاد گروه مهندسی آب، واحد لاهیجان، دانشگاه آزاد اسلامی، لاهیجان، ایران.)

تاريخ الإرسال : 26 الأحد , ربيع الثاني, 1444 تاريخ التأكيد : 02 الأربعاء , شعبان, 1444 تاريخ الإصدار : 29 الثلاثاء , شعبان, 1444

الکلمات المفتاحية: مقیاس پذیر, مدل‌ عملکرد برنج, توسعه پایدار کشاورزی, سنجش از دور, گوگل ارث انجین (GEE),

ملخص المقالة :

زمینه و هدف: گیاه برنج یکی از مهمترین محصولات استراتژیکی در کشور ایران محسوب می‌شود. از دیگر سو، کشاورزی طیف گسترده ای از امکانات و در عین حال مشکلات زیست محیطی را ایجاد می‌کند. درنتیجه، تحقیقاتی که به تولید و توسعه پایدار در این حیطه کمک ‌کنند، حائز اهمیت هستند. هدف اصلی از این پژوهش طراحی و توسعه مدل مقیاس پذیر عملکرد برنج مبتنی بر سنجش از دور و پردازش داده های ماهواره ای است.روش بررسی: در این مطالعه، از چندین تصویر مختلف، موجود در سامانه گوگل ارث انجین (Google Earth Engine) برای تخمین عملکرد برنج در مقیاس های مختلف زمانی (فصول رشد مختلف) و مکانی (وضوح 30 متر تا مقیاس های منطقه ای) استفاده شد. سپس یک مدل کارایی استفاده از نور (LUE) مبتنی بر سنجش از دور را اجرا نموده و در آن تنش‌های محیطی غیرزنده را وارد نمودیم. این مدل فیزیکی در برابر داده‌های عملکرد اندازه‌گیری شده زمینی، در سال‌های زراعی 1395، 1396 و 1398 در 691 مزرعه برنج در استان گیلان ارزیابی شد.یافته ها: نتایج، همبستگی مثبت و تطابق قابل توجهی بین مقادیر محاسباتی و مشاهداتی نشان داد، بطوریکه در سال‌های زراعی مورد مطالعه، میانگین ضریب همبستگی (R) و شاخص توافق (d) برابر با55/0 بدست آمد. میانگین RMSE برابر با 500 کیلوگرم در هکتار، میانگین MAE برابر با 440 کیلوگرم در هکتار، و میانگین NRMSE برابر با 0.12، حاکی از دقت مناسب مدل در برآورد عملکرد محصول در این مکان‌ها و سال‌هاست. مدل ارائه شده، تغییرپذیری مناسب مقادیر عملکرد را در مقیاس مزرعه نشان داد.بحث و نتیجه گیری: بهره‌گیری از سنجش از دور در محیط GEE به ‌عنوان ابزاری مفید جهت تخمین عملکرد محصول در مقیاس‌های مختلف زمانی و مکانی، مورد تایید قرار گرفت. مدل حاضر می‌تواند در طیف وسیعی از کاربردها مانند مدیریت کشاورزی و بیمه محصولات کشاورزی مورد استفاده قرار گیرد.

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  1. Okhovat, M., and D. Vakili. "Rice (cultivation, keep, Harvest). Tehran University P." 1996. (In Persian)
    2.
  2. Lobell DB. The use of satellite data for crop yield gap analysis. Field Crops Research. 2013 Mar 1;143:56-64.
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  3. Khaki, S.;Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci. 2019, 10, 621.
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  4. Kross, A.; McNairn, H.; Lapen, D.; Sunohara, M.; Champagne, C. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 235–248.
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  5. Scharf, P.C.; Lory, J.A. Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agron. J. 2002, 94, 397–404.
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