داده‌گواری سنجش از دور به‌‌روش جایگزینی در شبیه‌سازی عملکرد ذرت علوفه‌ای با استفاده از مدل AquaCrop

نوع مقاله : مقاله پژوهشی

نویسنده

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

چکیده

مقدمه: برآورد به‌موقع و دقیق عملکرد محصول قبل‌از برداشت و پیش‌بینی آن ازطریق مدل‌های رشد محصول، برای دستیابی به برنامه‌ریزی عملیات زراعی و حفظ و توسعۀ عملکرد در مقیاس منطقه‌ای، از اهمیت بسیاری برخوردار است. مدل‌سازی تغییرات پویا، در هنگام رشد محصول، کمک شایان توجهی به محققان می‌کند تا استراتژی‌های مدیریت محصول را به‌منظور افزایش عملکرد آن، برنامه‌ریزی کنند. این مدل‌ها حاوی پارامترهای متعددی است که باید، با توجه به ویژگی‌های منطقۀ مورد مطالعه، کالیبره شوند؛ ازطرفی، وجود نداشتن مؤلفۀ مکان در این مدل‌ها و نیز عدم‌قطعیت درمورد مقادیر پارامترهای آنها منجر به بروز خطا در خروجی‌های برآوردشده می‌شود. اسیمیلیت داده‌های سنجش از دور می‌تواند برای حل این مشکل و ارزیابی تغییرپذیری مکانی در اراضی، به‌ویژه در مقیاس منطقه‌ای، مفید باشد. سنجش از دور را می‌توان برای تخمین و برآورد مقادیر پارامترهای ورودی مدل‌های رشد محصول، مانند شاخص سطح برگ، سطح پوشش، بیومس، ویژگی‌های خاک به کار برد.
مواد و روشها: برای دستیابی به عملکرد دقیق محصول می‌توان از مدل‌های رشد گیاه استفاده کرد. برای تخمین پارامترهای مدل شبیه‌سازی گیاه زراعی AquaCrop و تنظیم مدل در سطح منطقه، اطلاعات مورد نیاز مدل در مراحل متفاوت رشد گیاه و قبل‌از کشت، در مزارع ذرت علوفه‌ای و در مقیاس منطقه‌ای، اندازه‌گیری و نمونه‌برداری شد. به‌منظور کالیبره کردن مدل شبیه‌سازی AquaCrop ازطریق داده‌گواری سنجش از دور (RS)، متغیر بیوفیزیکی fCover از داده‌های RS مبتنی‌بر پیکسل، با توسعۀ الگوریتم GPR-PSO، استخراج شد. علاوه‌براین، با هدف ساده‌سازی مدل AquaCrop و شناسایی پارامترهای تأثیرگذارتر، الگوریتم‌های تحلیل حساسیت ترکیبی Morris و EFAST به کار رفت. درنَهایت، ازطریق داده‌گواری متغیر بیوفیزیکی استخراج‌شده با RS در مدل AquaCrop، این پارامترهای مؤثرتر با استفاده از روش جایگزینی تخمین زده شد و نتایج با نتایج حاصل از شرایط استفاده نکردن از داده‌های RS مقایسه شد. به‌منظور کالیبره کردن مدل AquaCrop، نمونه‌برداری مزرعه‌ای از خاک (قبل‌از کاشت) و محصول در فصل رشد ذرت علوفه‌ای، عکس‌برداری رقومی نیم‌کروی (DHP) و همچنین اندازه‌گیری به‌روش تخریبی LAI برای مقایسه، در مزارع شهرستان قلعه‌نو واقع‌در جنوب تهران، در تابستان ۱۳۹۸ انجام شد.
نتایج و بحث: نتایج داده‌گواری RS در مدل AquaCrop در مقایسه با به کار نبردن داده‌های RS در این مدل نشان داد که در نظر گرفتن داده‌گواری RS منجر به افزایش دقت تنظیم کردن مدل می‌شود. نتایج نشان داد که داده‌گواری سنجش از دور در مدل به برآورد دقت متغیر خروجی عملکرد در آمارۀ R2، به‌میزان ۸۹/۰ و ۸۸/۰، در واسنجی و صحت‌سنجی منجر شده است. داده‌گواری سنجش از دور، در قیاس با اعمال نشدن آن، به بهبود دقت و افزایش R2 به‌میزان ۱۴/۰ و ۱۵/۰ و نیز کاهش در آمارۀ‌ RRMSE به‌میزان ۱۲/۴ و ۱۷/۵%، در آمارۀ RMSE به‌میزان ۵/۲ و ۴/۲ ton/ha، به‌ترتیب در واسنجی و صحت‌سنجی، انجامیده است. بنابراین، در مقایسۀ داده‌گواری RS و بدون داده‌گواری، بهبود فرایند تنظیم مدل با داده‌گواری RS همراه است.
نتیجه‌گیری: در این تحقیق، مقادیر برآوردشدۀ پارامتر بیوفیزیکی fCover، به‌دست‌آمده ازطریق سنجش از دور به‌منزلۀ متغیر کنترل مشاهداتی ورودی برای مدل AquaCrop استفاده شد تا پارامترهای تأثیرگذار شناسایی‌شدۀ آن (ازطریق تحلیل حساسیت) تنظیم شود. نتایج نشان می‌دهد که داده‌گواری سنجش از دور، با استفاده از روش جایگزینی برای تنظیم مدل مدنظر، توانسته است بر میزان دقت برآوردشده بیفزاید. علاوه‌براین، توافق بین مقادیر پیش‌بینی‌شده و اندازه‌گیری‌شده بیشتر از زمانی ‌است که سنجش از دور اعمال نمی‌شود. بنابراین نتایج تحقیق نشان می‌دهد که داده‌گواری سنجش از دور در مدل AquaCrop می‌تواند عملکردی موفق‌تر از شرایط اعمال نشدن سنجش از دور داشته باشد و نتایج با دقت بیشتری به دست دهد. همچنین، در مقیاس منطقه‌ای، می‌توان با استفاده از سنجش از دور و قابلیت آن در برآورد پارامتر بیوفیزیکی در مقیاس وسیع، با صرف وقت و هزینۀ کمتر و به‌روزتر، مدل‌های رشد محصول را برای منطقۀ مورد نظر کالیبره کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Remote Sensing Data Assimilation by Forcing Method in Simulation of Silage Maize Yield Using AquaCrop Model

نویسنده [English]

  • Elahe Akbari
Dep. of Remote Sensing and Geographic Information System, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
چکیده [English]

Introduction: An essential part of agricultural plans for maintaining and developing performance at the regional scale is the timely and accurate estimation and prediction of crop yield prior to harvesting using crop growth models. Modeling dynamic changes during crop growth helps researchers to plan crop management strategies to improve its yield. These models contain several parameters that should be calibrated according to the characteristics of the study area. Lack of spatial/geographic components in these models and parameter uncertainties lead to errors in the estimated outputs. Remote sensing data assimilation can be useful for solving this problem and evaluating the spatial variability in the lands, especially at the regional scale. Remote sensing can estimate the values of input variables of crop growth models such as the Leaf Area Index (LAI), canopy cover, biomass, and soil characteristics.
Materials and Methods: To achieve accurate crop yield, it is possible to use crop growth models. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing stages and prior to cultivation over agricultural silage maize fields at the regional scale. To calibrate the Aquacrop simulation model through assimilation of remote sensing (RS) data, fCover biophysical variable was extracted from pixel-based RS data by developing GPR-PSO algorithm. Besides, to simplify the Aquacrop model, and to identify more sensitive parameters, the combined sensitivity analysis Morris and EFAST algorithms were employed. Finally, by assimilating the biophysical variable extracted by RS into the Aquacrop model, these more effective parameters were estimated through the forcing method, and compared the results with the results of no application of RS data. In order to calibrate the Aquacrop model, field sampling of soil (before planting) and crop during the growing season of silage maize, digital hemispherical photography (DHP) as well as measurement by destructive method for comparison, was performed in the fields of Qhale-Nou county located in the south of Tehran, in the summer of 2019.
Results and Discussion: The results of assimilation of RS data in Aquacrop model compared to no application of RS data in this model showed that considering data assimilation of RS data leads to the increase in model calibration accuracy. As the results suggest, the output yield for the model with data assimilation was estimated with R2 values of 0.89 and 0.88 for calibration and evaluation, respectively. The superiority of RS data assimilation into the model as opposed to not its incorporating was also verified by improving the accuracy with increases in R2 values by 0.14 and 0.15 and decrese in Relative RMSE (RRMSE) values of 4.12 and 5.17 percent and RMSE of 2.5 and 2.4 ton/ha for calibration and evaluation, respectively. So, compared to RS data assimilation and without assimilation is associated with improving the model calibration process with RS data assimilation.
Conclusion: The present study employed estimated fCover values obtained via RS data as observed state variables fed as input to the AquaCrop model for means of estimating the most effective parameters identified (via sensitivity analysis). The findings of this procedure indicate that RS data assimilation as a forcing method for model parameters estimating can increase the overall accuracy of the model. Moreover, the correlation between simulated and observed values was higher for the case of RS data assimilation as opposed to not incorporating such data. As these results suggest, RS data assimilation into the AquaCrop model can prove more successful and attain higher accuracies as opposed to not incorporating such data. Furthermore, this process of data assimilation can be used for estimating biophysical variables and calibrating crop growth models at the regional scale, with less time complexity and lower costs and more updated information.

کلیدواژه‌ها [English]

  • AquaCrop
  • Crop growth simulation model
  • Forcing method
  • fCover
  • Remote sensing
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