مروری بر داده‌گواری سنجش از دور در مدل‌های شبیه‌سازی رشد گیاه زراعی

نوع مقاله : مروری

نویسنده

گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران

چکیده

برآورد دقیق و به موقع عملکرد محصول قبل از برداشت و پیش‌‌بینی آن از طریق مدل‌‌های رشد محصول، برای دستیابی به برنامه‌‌ریزی عملیات زراعی و حفظ و توسعه عملکرد در مقیاس منطقه‌‌ای، از اهمیت زیادی برخوردار است. مدلسازی تغییرات پویا در هنگام رشد محصول کمک شایان توجهی به محققین می‌‌نماید تا راهکارهای مدیریت محصول را به منظور افزایش عملکرد آن برنامه‌‌ریزی کنند. این مدل‌‌ها حاوی پارامترهای متعددی بوده که بایستی با توجه به ویژگی‌‌های منطقه مورد مطالعه تنظیم شوند، از طرفی فقدان مولفه مکان در این مدل‌‌ها و نیز عدم قطعیت در مورد مقادیر پارامترهای آنها، منجر به بروز خطا در خروجی‌‌های برآورد شده می‌‌شود. داده‌‌گواری سنجش از دور می‌‌تواند برای حل این مشکل و ارزیابی تغییرپذیری مکانی در اراضی بویژه در مقیاس منطقه‌‌ای مفید باشد. سنجش از دور برای تخمین و برآورد مقادیر پارامترهای ورودی مدل‌‌های رشد محصول نظیر شاخص سطح برگ، سطح پوشش، زیست توده گیاه، خصوصیات خاک می‌‌تواند استفاده شود. در این تحقیق، روش‌‌های مختلف داده‌‌گواری سنجش از دور در مدل‌‌های رشد محصول معرفی، مقایسه و مزایا و معایب هر کدام بررسی می‌‌شود. علاوه بر این، مروری بر تحقیقاتی که در این زمینه اجرا شده می‌‌تواند به خوانندگان در مورد انتخاب نوع مدل رشد محصول، روش داده‌‌گواری سنجش از دور، متغیر حالت (کنترل) مورد استفاده کمک نماید. مطالعه تحقیقات مختلف نشان می‌‌دهد که با آمدن سنجنده‌‌ها و روش‌‌های جدید در برآورد متغیرهای حالت (کنترل) سنجش از دوری نظیر شاخص سطح برگ و نیز توسعه و بهبود مدل‌‌های رشد محصول، می‌‌توان دقت تخمین عملکرد محصول را بهبود بخشید.

کلیدواژه‌ها


عنوان مقاله [English]

A review of Remotely Sensed Data Assimilation into Crop Simulation Models

نویسنده [English]

  • Elahe Akbari
Department of Remote sensing and Geographic Information System, Faculty of Geography and Environmental sciences, Hakim sabzevari University, Sabzevar, Iran
چکیده [English]

A significant course of action to planning agricultural operations and further maintaining and developing performance on a regional scale involves the accurate and timely estimation of crop yield prior to harvesting using crop growth models. Modeling dynamic changes during crop growth can assist researchers in planning crop management strategies aimed at increasing crop yield. Such models include several parameters that can be calibrated according to the characteristics of the study area. However, insufficent information on location/spatial-wise components or the lack of  thereof in these models along with uncertainties in parameter values may lead to errors in the estimated outputs. In this light, remote sensing data assimilation can be useful for resolving such complications and evaluating the spatial variability of lands, particularly at the regional scale. Remote sensing can estimate values of input parameters for crop growth models such as Leaf Area Index (LAI), fCover, biomass, and soil characteristics. This review paper seeks to introduce and compare different methods of remote sensing data assimilation in crop growth models and examine their advantages and disadvantages. In addition, a literature review conducted in this field can guide the readers in slecting the appropriate crop growth model, relevant remote sensing data assimilation method, and pertinent state/control variables. The literature review indicates that with new sensors and methods in the estimation of remote sensing state/control variables such as LAI and the development and improvement of crop growth models, it is possible to improve the accuracy of crop yield estimation.

کلیدواژه‌ها [English]

  • remote sensing data assimilation
  • calibration
  • crop model
  • forcing
  • updating

A review of Remotely Sensed Data Assimilation into Crop Simulation Models

EXTENDED ABSTRACT

 

Background and aim

A significant course of action to planning agricultural operations and further maintaining and developing performance on a regional scale involves the accurate and timely estimation of crop yield prior to harvesting using crop growth models. Modeling dynamic changes during crop growth can assist researchers in planning crop management strategies aimed at increasing crop yield. Such models include several parameters that can be calibrated according to the characteristics of the study area. However, insufficent information on location/spatial-wise components or the lack of  thereof in these models along with uncertainties in parameter values may lead to errors in the estimated outputs. In this light, remote sensing data assimilation can be useful for resolving such complications and evaluating the spatial variability of lands, particularly at the regional scale. Remote sensing can estimate values of input parameters for crop growth models such as Leaf Area Index (LAI), fCover, biomass, and soil characteristics.

 

Methodology

This review paper seeks to introduce and compare different methods of remote sensing data assimilation in crop growth models and examine their advantages and disadvantages. In addition, a literature review conducted in this field can guide the readers in slecting the appropriate crop growth model, relevant remote sensing data assimilation method, and pertinent state/control variables.

 

Findings

As the most promising of approaches, remote sensing methods are used for the assimilation of canopy state/control variables and soil properties in crop growth models and further enhancement of crop management. Given the rapid development of remote sensing data with high spatial and temporal resolution, these methods can be employed to improve the dynamic time-series simulation of crop growth models and further increase the accuracy of simulating canopy state/control variables and soil properties in crop models. In addition, other improvements in accuracy of estimated canopy state/control variables and soil properties through UAVs and the rapid development of versatile, lightweight, and low-cost portable sensors can provide additional remote sensing data at high spatial and temporal resolution for crop growth models used in field scales. Reviewing the literature shows that with new sensors and methods in the estimation of remote sensing state/control variables such as the LAI and the development and advancement of crop growth models, there is a potential to improve the accuracy of crop yield estimation.

 

Conclusion

Since well-timed and accurate estimations of crop growth processes as well as crop condition and yield is essential for making farm management decisions prior to harvesting, this paper proceeded with a discussion on different remote sensing data assimilation methods for crop growth models and crop yield estimation on a regional scale. Moreover, the advantages and disadvantages of three remote sensing data assimilation methods in crop growth models (calibration method, forcing method, and updating method) were mentioned. Considering the type of data, type of crop growth model, calculation time, phenological shift or non-shift of data used for simulating the model with remote sensing data, availability of remote sensing data in critical times of crop growth for a more accurate simulation of the crop growth process, and finally the user's ability to program and model the simulation of the crop growth process, it is possible to decide on the appropriate remote sensing data assimilation method required for the corresponding crop growth model.

 

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