برآورد محصول و کاه گندم دیم با استفاده از تصاویر landsat-oli

پیش‏بینی عملکرد محصول از مهم‌ترین ابزارهای برنامه‏ریزی به‌منظور تامین به‌موقع محصولات زراعی، مخصوصاً محصول استراتژیک گندم، است. در این تحقیق پیش‏بینی عملکرد گندم دیم در بخشی از شهرستان گیلان‏غرب با استفاده از شاخص‏های گیاهی  ndvi و glai و داده‏های زمینی عملکرد گندم دیم و کاه مربوط به 35 قطعه زمین زراعی براساس ایجاد رابطة رگرسیون چندمتغیره بین شاخص‌های گیاهی و داده‌های زمینی در سال‌های زراعی 20142018 انجام گرفت. در بازة زمانی 20142018، نمودار دورة رشد محصول با استفاده از هر شاخص رسم شد و پارامتر هندسی مربوط به منحنی رشد گیاه مانند مساحت زیرنمودار از آن‌ها استخراج شد. نتایج این تحقیق نشان داد glai ضریب تعیین بیشتری نسبت به شاخص ndvi دارد. همچنین، رابطة رگرسیون چندمتغیره با 865 .0r2= برای برآورد میزان کاه و یک رابطه با 851 .0r2= برای گندم به‌دست آمد که درنهایت با استفاده از این روابط مقدار گندم برای کل منطقه برابر 295.606 تن و مقدار کاه برابر 705.032 تن برآورد شد. از بین مراحل مختلف رشد گیاه نیز مرحلة تشکیل گل‌آذین با 65 .0r2= بیشترین ضریب تعیین جهت برآورد میزان محصول گندم و کاه را به خود اختصاص داد.

Estimating of Biomass and Wheat Dry-Farming Using Landsat OLI Imagery

IntroductionWith a dominant arid to semiarid climate, Iran enjoys a diverse agroecosystem. Despite the country’s enormous territorial span, its agricultural lands encompass areas with limited precipitation and ground water resources. Dryfarming is a common practice in Iran, which faces certain challenges in areas like preharvest estimation of straw and crop yields. Sustainable agricultural activities require precise information on crops, which can be obtained from remote sensing data. This study proposes a remote sensing vegetation indexbased phenology modeling to estimate the straw and crop yield of dryfarmed wheat via Landsat OLI imagery in Gilangharb of Kermanshah province in Iran.Materials and MethodsA satellitebased straw and crop yield estimation method was developed for dryfarmed wheat, using Landsat OLI imagery. Field data were measured in metric ton per hectare through farmbased measurements of the net weight of wheat crop and straw, produced in dryfarming. The data were obtained through direct field surveys during the harvesting time. Using GPS, the study managed to single out the wheat farms from their surrounding farmlands. It also used time series of Landsat8 satellite imagery from midFebruary to lateMay in the study years (20142018). Once the images got preprocessed, they were classified via a multitemporal image classification procedure, where Normalized Difference Vegetation Index (NDVI) and Green Leaf Area Index (GLAI) were adapted as vegetation indices for wheat phenology modeling to be linked with the measured straw and crop yields. Annual phenology curves of both indices for each farm were statistically investigated, using the geometric characteristics of the phenology curve. The statistical relation between phenology curves and straw and crop yield was then calculated. In order to evaluate the results accuracy, fieldmeasured data on straw and crop yield were compared with the obtained results.Results and DiscussionKappa coefficient and overall accuracy were calculated using classification error matrix in order to evaluate the overall accuracy of image classification. Results accuracy was assessed, using the area of the curve of phenology diagrams for both vegetation indices of all wheat farms as well as the regression relations and coefficient of determination (R2) between indices and the measured wheat crop and straw. From the five phonological growth stages of wheat, namely germination/emergence, tillage, stem elongation, boot, heading/flowering, and grainfill/ripening, the penultimate stage (flowering) had the highest correlation with the wheat crop and straw. The study results revealed that green leaf area index (GLAI) had a higher coefficient of determination than NDVI. GLAI represents the main part of the photosynthesis in plants (leaf), the main factor for growth process of wheat. Hence, it had closer association with the plant’s production process. Therefore, GLAI outperformed NDVI in wheat phenology modeling for crop and straw estimation, though both indices were employed in the modeling since the main goal of this study was to obtain a more precise multivariate regression correlation. ConclusionUsing a multivariate regression analysis along with both GLAI and NDVI, the straw and crop yield of dryfarmed wheat was estimated with a high coefficient of determination (about 0.8). This coefficient was slightly higher for straw (R2=0.865) than wheat crop. Results of phenology investigation showed the model’s ability to estimate the wheat yield. Furthermore, it was revealed that out of the five phonological growth stages of wheat, the flowering stage (R2=0.65) had the highest correlation coefficient.