ارزیابی قابلیت تصاویر ماهواره‌ای Sentinel-2 در تخمین میزان بایومس محصول ذرت علوفه‌ای منطقه مورد مطالعه: شرکت کشاورزی و دامپروری مگسال (قزوین)

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناس مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران

2 استادیار مرکز مطالعات سنجش از دور و GIS، دانشگاه شهید بهشتی

چکیده

استفاده از داده‌های ماهواره‌ای، در برآورد دقیق مقدار بایومس محصول به‌عنوان یکی از مهم‌ترین چالش‌های سنجش از دور محیطی محسوب می‌شود. اگرچه، به طور سنتی از شاخص‌های طیفی پوشش‌گیاهی استخراج شده از باندهای قرمز (R) و مادون قرمز نزدیک (NIR) برای برآورد آماری بایومس محصول استفاده شده است، اما بیشتر این شاخص‌ها در مقادیر خاصی از شاخص سطح برگ اشباع می‌شوند. لذا به‌منظور غلبه بر محدودیت اشباع­شدگی، اخیرا مطالعات زیادی بر روی استفاده از بازتابندگی طیفی در محدوده لبه قرمز انجام شده است. برای ارزیابی عملکرد شاخص‌های مختلف پوشش‌گیاهی در برآورد بایومس محصول، پنج نوبت نمونه برداری از ویژگی‌های بیوفیزیکی ذرت علوفه‌ای در طول دوره رشد این محصول در اراضی زراعی شرکت کشت و صنعت مگسال، قزوین انجام شد و جمعا 182 نمونه میدانی جمع‌آوری شد. سپس 10 شاخص طیفی از سری زمانی تصاویر Sentinel-2 که همزمان با نوبت‌های نمونه برداری میدانی در سال 2017 اخذ شده بودند، محاسبه شده و با استفاده از آنها بایومس ذرت علوفه‌ای برآورد شد. بایومس ذرت علوفه‌ای با اندازه‌گیری‌های میدانی مورد ارزیابی قرار گرفت. نتایج نشان داد شاخص  با ضریب تعیین   و کمترین ریشه میانگین مربعات خطا ، بهترین شاخص برای تخمین بایومس ذرت علوفه‌ای است. علاوه بر این، تحقیق حاضر نشان داد که تصاویر ماهواره‌ای Sentinel-2 با توان تفکیک مکانی بالا و محدوده لبه قرمز، قابلیت تخمین مقدار بایومس محصول ذرت علوفه‌ای را با دقت مناسب دارد. 

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of Sentinel-2 imagery for the estimation of Silage maize biomass: A case study of Magsal Animal Husbandry & Agriculture, Qazvin, Iran

نویسندگان [English]

  • Farzaneh Hadadi 1
  • Hossain Aghighi 2
  • Ayoub Moradi 1
1 Remote sensing expert, Iranian Space Research Center
2 Assistant Professor in Remote Sensing, Remote Sensing and GIS Research Center
چکیده [English]

The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that  index with  and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that  index with  and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. 

کلیدواژه‌ها [English]

  • remote sensing
  • Time Series Analysis
  • Red edge Index
  • Biomass estimation
  • Silage maize
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