برآورد محتوی رنگدانه‌های برگ گندم زمستانه (Triticum aestivum L.) با استفاده از تصاویر ماهواره لندست 8

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری فیزیولوژی گیاهان زراعی، دانشکده کشاورزی، دانشگاه شهرکرد، شهر کرد، ایران

2 دانشیار، گروه خاکشناسی، دانشکده کشاورزی، دانشگاه شهرکرد، شهر کرد، ایران

3 دانشیار، گروه زراعت، دانشکده کشاورزی، دانشگاه شهرکرد، شهر کرد، ایران

4 استادیار، گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهر کرد، ایران

چکیده

سابقه و هدف: محتوای کلروفیل برگ‌ها در گیاهان معرف چگونگی سلامتی، وضعیت فیزیولوژیکی، فعالیت فتوسنتزی و میزان نیتروژن آن‌هاست. نظارت بر محتوای رنگ‌دانه‌های گیاهان زراعی به شناسایی وضعیت تغذیه گیاه و تنش‌های محیطی قبل از آسیب جدی به محصول و عملکرد کمک می‌کند. این تحقیق به منظور تخمین محتوای رنگ‌دانه‌های برگ گندم (Triticum aestivum L.) به عنوان یکی از مهم‌ترین محصولات زراعی به کمک داده‌های ماهواره لندست 8 و مدل‌های آماری در شهرستان شهرکرد، استان چهارمحال و بختیاری در سال 1396 انجام شد.
مواد و روش‌ها: برای این منظور هشت مزرعه زیر کشت گندم زمستانه با مساحت بین 10 تا 60 هکتار در سراسر شهرستان شهرکرد، در نظر گرفته شد. سپس موقعیت 120 واحد نمونه‌برداری به صورت تصادفی در مزارع مورد مطالعه توسط GPS تعیین گردید. واحدهای نمونه‌برداری به صورت مربع‌های 30 × 30 متری مطابق با پیکسل‌های ماهواره لندست بود. در هر واحد 5 پلات (5/0 × 5/0 متر) در چهار گوشه و مرکز مربع در نظر گرفته شد. جمع‌آوری نمونه‌ها هم‌زمان با عبور ماهواره انجام شد. نمونه‌ها به آزمایشگاه منتقل و محتوای کلروفیل و کارتنوئید اندازه‌گیری شد. داده‌های تصاویر ماهواره لندست 8 مربوط به زمان نمونه‌برداری پردازش و شاخص‌های گیاهی محاسبه شد. برای به‌دست آوردن مدل‌های برآورد محتوای کلروفیل و کاروتنوئید برگ گندم از روش-های رگرسیون‌های خطی ساده و چندگانه گام به گام استفاده گردید.
یافته‌ها: نتایج نشان داد که در بین شاخص‌های انتخاب شده، شاخص PSRI برای برآورد محتوای کلروفیل a (408/0 = R2و µg.cm-2 38/8 = RMSE)، کلروفیل b (400/0 = R2و µg.cm-2 69/3 = RMSE) و کلروفیل کل برگ (500/0 = R2و µg.cm-2 82/10 = RMSE)، شاخص CRI (480/0 = R2و µg.cm-2 92/2 = RMSE) برای برآورد محتوای کاروتنوئید برگ و شاخص SIPI (603/0 = R2و µg.cm-2 17/0 = RMSE) برای برآورد نسبت کاروتنوئید به کلروفیل a در رگرسیون خطی ساده، دقت بهتری داشتند. مدل‌های مبتنی بر رگرسیون خطی چندگانه گام به گام محتوای کلروفیل a را با 557/0 = R2 و µg.cm-2 73/7 = RMSE، کلروفیل b را با 471/0 = R2 و µg.cm-2 69/3 = RMSE، کلروفیل کل را با 611/0 = R2 و µg.cm-2 10/10 = RMSE، کاروتنوئید را با 500/0 = R2و µg.cm-2 01/2 = RMSE و نسبت کاروتنوئید به کلروفیل a را با 756/0 = R2 و µg.cm-2 12/0 = RMSE برآورد کرد. شاخص‌های PSRI و CVI برای برآورد محتوای کلروفیل a و کل، شاخص‌ PSRI برای برآورد محتوای کلروفیل b، شاخص‌های CRI و TCI/OSAVI برای برآورد محتوای کاروتنوئید و شاخص‌های SIPI، GNDVI، EVI، CIgreen، TCARI و OSAVI برای برآورد نسبت کاروتنوئید به کلروفیل a برگ گندم مؤثرترین شاخص‌ها در مدل رگرسیون خطی چندگانه گام به گام بودند. در مطالعه ما برآورد محتوای کلروفیل a، کل، کاروتنوئید و نسبت کاروتنوئید به کلروفیل a برگ بر اساس رگرسیون خطی چندگانه گام به گام نسبت به مدل رگرسیون خطی ساده نتایج بهتری را نشان دادند. برآورد محتوای کلروفیل b در هر دو روش یکسان بود.
نتیجه‌گیری: استفاده از شاخص‌های گیاهی حاصل از داده‌های لندست 8 امکان برآورد محتوای رنگ‌دانه‌های برگ گندم را در منطقه مورد مطالعه با نتایج نسبتاً خوبی فراهم می‌کند. چنین اطلاعاتی برای کشاورزان به منظور پایش اولیه ضعیت کیفیت و کمیت تولید اهمیت داشته و منجر به یک برنامه‌ریزی کاربردی و کارآمد در روند کوددهی می‌شود.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of winter wheat (Triticum aestivum L.) leaf pigments content using Landsat‑8 imagery

نویسندگان [English]

  • Maryam Soltanian 1
  • Mehdi Naderi Khorasgani 2
  • Ali Tadayyon 3
  • Mozhgan Abbasi 4
1 Student/Sharekord university
2 Pedology Department, Faculty of Agriculture, Shahrekord University, Shahrekord, IRAN.
3 Associate Professor, Agronomy Department., Faculty of Agriculture, Shahrekord University
4 Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.
چکیده [English]

Background and objectives: Leaf chlorophyll content in plants indicates their health, physiological status, their photosynthetic activity and nitrogen content. Monitoring pigment content of crop tissues helps identification of plant nutrition and environmenta stresses before serious damages to crop and yield. This study was conducted to estimate the leaf pigments content of winter wheat (Triticum aestivum L.) as one of the most important crops using Landsat 8 satellite data and statistical models in Shahrekord county, Chaharmahal and Bakhtiari province in 2017.
Materials and methods: For this purpose, eight fields under winter wheat cultivation with an area between 10 to 60 hectares throughout Shahrekord county were considered. The location of 120 sampling units was randomly and determined in fields using ground positioning system (GPS). The sampling units were 30 × 30 m squares according to Landsat pixels. In each unit 5 plots (0.5 × 0.5 m) were considered, four plots in the corners and one in the center of the unit. Crop sampling and passing over of satellite were synchoronous. The samples were transferred to the laboratory and their chlorophyll and carotenoid content were measured. The corresponded Landsat 8 data were processed and and vegetation indices were calculated. Simple and multiple stepwise linear regression methods were used to obtain models for estimating chlorophylls and carotenoids content of wheat leaves.
Results: The results showed that among the selected indices, PSRI index for estimating chlorophyll a content (R2 = 0.408 and RMSE= 8.38 µg.cm-2), chlorophyll b (R2 = 0.400 and RMSE= 3.69 µg.cm-2) and total chlorophyll (R2 = 0.500 and RMSE= 10.82 µg.cm-2), CRI index (R2 = 0.480 and RMSE= 2.92 µg.cm-2) for estimating leaf carotenoid content and SIPI index (R2 = 0.603 and RMSE = 0.17 µg.cm-2) for estimating carotenoid to chlorophyll a ratio had the best performance in simple linear regression. Models based on multiple stepwise linear regression estimated chlorophyll a content with R2 = 0.557 and RMSE = 7.73 µg.cm-2, chlorophyll b with R2 = 0.471 and RMSE = 3.69 µg.cm-2, total chlorophyll with R2 = 0.611 and RMSE = 10.10 µg.cm-2, carotenoids with R2 = 0.50 and RMSE = 2.01 µg.cm-2 and carotenoid to chlorophyll a ratio with R2 = 0.756 and RMSE = 0.12 µg.cm-2. PSRI and CVI indices for estimating chlorophyll a and total chlorophyll content, PSRI index for estimating chlorophyll b content, CRI and TCI / OSAVI indices for estimating carotenoid content and SIPI, GNDVI, EVI, CIgreen, TCARI and OSAVI for carotenoid to chlorophyll a ratio of wheat leaf were the most effective indices in a stepwise multiple linear regression models. In our study, estimation of chlorophyll a, total, carotenoid content and carotenoid to chlorophyll a ratio based on stepwise multiple linear regression was superior to simple linear regression models. Estimation of chlorophyll b content was the same in both methods.
Conclusion: The use of vegetation indices derived from Landsat 8 data makes it possible to estimate the content of wheat leaf pigments with relatively good results in the study area. Such information is important for farmers to initially monitor the quality and quantity of production and leads to a practical and efficient planning in the fertilization process.

کلیدواژه‌ها [English]

  • Carotenoids
  • Chlorophyll
  • Remote Sensing
  • Vegetation indices
  • Wheat

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    Xue, J., and Su, B. 2017. Significant 

مجله تولید گیاهان زراعی
دوره 14، شماره 3
مهر 1400
صفحه 85-106

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