مقایسه روش‌های زمین‌آمار و سنجش از دور در برآورد عملکرد دانه برنج (Oryza sativa L.) در استان گیلان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه زراعت، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، ایران.

2 گروه اگروتکنولوژی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

3 گروه مهندسی آب، دانشکده فنی، دانشگاه آزاد اسلامی، واحد لاهیجان، ایران.

4 مؤسسه تحقیقات برنج کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، رشت، ایران.

چکیده

به‌منظور مقایسه روش‏های مبتنی بر سنجش از دور و زمین‌آمار برای برآورد عملکرد برنج (Oryza sativa L.) اراضی کشت برنج استان گیلان، پژوهشی در سال­های زراعی 1395 و 1396 انجام شد. جهت انجام عملیات میدانی، 320 نمونه عملکرد دانه در مرحله رسیدگی فیزیولوژیکی گیاه برنج از 238 هزار هکتار اراضی برنج برداشت شد. در این تحقیق، از 33 روش-مدل زمین‌آمار برای درون‌یابی مقدار عملکرد دانه استفاده شد و سپس دقت روش‌های درون‌یابی با کمک معیارهای آماری مختلف ارزیابی شد. در روش مبتنی بر سنجش از دور، از تصاویر سنجنده تصویربردار عملیاتی زمین (OLI) ماهواره لندست-8 و تصاویر سنجنده تصویربردار ماهواره سنتینل-2 استفاده شد. هشت شاخص­ گیاهی از تصاویر استخراج و ارتباط بین آن‌ها و متغیر عملکرد با استفاده از رابطه رگرسیونی استخراج و نقشه عملکرد با کمک تصاویر تهیه و ارزیابی شد. نتایج ارزیابی روش‌های درون‌یابی بیان‌گر برتری روش-مدل کریجینگ معمولی - stable نسبت به دیگر مدل‌ها بود. در این مطالعه، شاخص RVI در بین شاخص­های گیاهی بیش­ترین دقت را در پیش­بینی عملکرد اراضی برنج داشته است. مقایسه روش زمین­آمار و روش مبتنی بر سنجش از دور بر اساس ضریب تبیین و جذر میانگین مربعات خطا در برآورد عملکرد دانه در سطح استان گیلان نشان داد که هر دو روش دقت قابل قبولی داشتند، امّا با توجه به توانایی سنجش از دور در تفکیک نقطه­ای بازتاب نوری پدیده­ها و پیش­بینی عملکرد با تفکیک مکانی بالا، در این روش دقت بالاتری در تخمین عملکرد حاصل شد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of Geostatistical Method and Remote Sensing in Estimating Rice (Oryza sativa L.) Grain Yield in Guilan Province

نویسندگان [English]

  • Pooya Aalaee Bazkiaei 1
  • Behnam Kamkar 2
  • Ebrahim Amiri 3
  • Hossein Kazemi 1
  • Mojtaba Rezaei 4
1 Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan. Iran.
2 Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
3 Department of Water Engineering, Lahijan branch, Islamic Azad University, Iran.
4 Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran.
چکیده [English]

Introduction
 Food security has been the most important human concern on the planet. Food security and future changes in food prices and crop development undeniably depend on the average yield of crops. Rice is the second largest crop in terms of area under cultivation and provides food for more than half of the world's population. Due to the importance of rice production, it is important to monitor its production on a large scale. Remote sensing and geostatistical methods play an important role in spatial and temporal evaluation of climatic, soil, living factors and management methods. The study of the amount of yield obtained in province of Guilan can provide appropriate information in advancing/pursuing goals such as the study of the amount of yield gap in this region; therefore, this study was conducted to evaluate the yield of rice using two methods of geostatistics and remote sensing approach in 2016 and 2017 in Guilan province.
Materials and Methods
 In this study, different methods of preparing vegetation map to determine rice yield map were evaluated. To compare methods based on satellite-imagery and geostatistical procedure-models to estimate the rice yield of rice cultivated lands in Guilan province, a study was conducted in the 2016 and 2017. For field operations, 320 fields were surveyed to record grain yield (of total 238,000 hectares of rice-grown fields) during the physiological maturity stage. In this study, 33 statistical procedure-models were used to interpolate the amount of grain yield and then the accuracy of interpolation methods were evaluated using various statistical criteria. Satellite-imagery based methods using Landsat 8 satellite Operational Land Imager (OLI) sensor images related to the dates of June 18, August 9, August 21 in 2016, and July 23, August 8 in 2017 and the images of the Sentinel-2 satellite on June 30 and September 13, 2017, were used. Eight satellite-derived vegetation indices were calculated and the relationship between them and the yield variable was extracted using the regression relationship, and the yield map was prepared and evaluated. By fitting the peak logistics model between yield values and vegetation indices and selecting the superior index, the yield map was prepared with the help of remote sensing and the obtained yield maps were compared with different statistical criteria.
Results and Discussion
 The results of the evaluation of geostatistical interpolation methods showed the superiority of the ordinary stable kriging procedure-model over other models. In this study, the RVI vegetation index was selected as the superior index to predict actual yield throughout the Guilan Province. Comparison of geostatistical procedure-models and satellite-imagery oriented models based on the determination coefficient of regressed equations and root mean square error (RMSE) to estimate grain yield in Guilan province showed that both procedures had acceptable accuracy, however, due to the ability of remote sensing to distinguish the pointwise optical reflection of phenomena and to predict yield with high spatial resolution, this method achieved higher accuracy in yield estimating.
Conclusion
 Comparison of geostatistical and remote sensing methods in predicting farm yield indicated that the remote sensing method was more accurate. Early harvest forecasting based on the information extracted from this study showed that the use of image / images obtained in June and August and OLI sensor of Landsat-8 satellite in Guilan province can be used as a basis for forecasting the yield of this plant in the coming years. Such studies in Guilan province, by considering the share of Guilan province in the country's rice supply, will play an effective role in managerial decisions regarding rice supply and demand at the macro level. The results also showed that sampling in the study area based on a regular (systematic) spatial pattern can increase the accuracy of geostatistical methods in estimation of regional yield. The results of this project can provide suitable basic information for other studies such as yield gap, the reasons behind it, the relationship between land suitability and the obtained yield and forecasting and estimating yield in different time periods.
 

 
 
 
 
 
 

کلیدواژه‌ها [English]

  • Actual yield
  • Kriging
  • Landsat-8
  • Sentinel-2
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