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

محورهای موضوعی : آلودگی خاک

احسان آسمار ¹ , محمد حسن وحید نیا ² , مجتبی رضایی ³ , ابراهیم امیری ⁴

1 - دانشجوی دکتری گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
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)
3 - استادیار موسسه تحقیقات برنج کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، رشت، ایران.
4 - استاد گروه مهندسی آب، واحد لاهیجان، دانشگاه آزاد اسلامی، لاهیجان، ایران.

تاریخ دریافت : 1401/08/29 تاریخ پذیرش : 1401/12/03 تاریخ انتشار : 1402/01/01

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

چکیده مقاله :

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.

چکیده انگلیسی:

Background and Objective: Rice is one of the most strategic plants in Iran. On the other hand, agriculture makes a wide variety of environmental amenities and problems. Thus researches that help the production and sustainable development in this area are significant. The main purpose of this research is the design and development of a scalable remote sensing-based paddy yield model.Material and Methodology: In this study, we used several different images available in Google Earth Engine (GEE) to estimate paddy yield at various temporal (growing seasons) and spatial scales (from 30 m resolution to regional scales). Then, a remote sensing-based light use efficiency (LUE) model integrated with inanimate environmental stressors, was implemented. This operational model was assessed against actual field-level yield data in 2016, 2017, and 2019 growing seasons across more than 691 paddy fields in Gilan province.The efficiency of the current model was evaluated through different statistical measures. The results showed a positive correlation and a signed agreement between the estimated and measured values so that in the studied growing seasons, the average correlation coefficient (R) and agreement index (d) was equal to 0.55. The average RMSE equal to 500 kg/ha, the average MAE equal to 440 kg/ha, and the average NRMSE equal to 0.12, all indicate that the accuracy of the model in estimating crop yield in these locations and years is satisfactory. Also, the submitted model showed the appropriate variability of yield values at the farm scale.Discussion and conclusion: In general, this new approach has confirmed that the use of remote sensing in the GEE is appropriate for estimating crop yield at various temporal and spatial scales, as the current model can be utilized in a wide range of applications such as agricultural management and insurance.  

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