تخمین تولید محصول کلزا مبتنی‌بر سری زمانی داده‌های سنجش از دور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار مرکز مطالعات سنجش از دور وGIS ، دانشکدة علوم زمین، دانشگاه شهید بهشتی

2 دانشجوی دکتری مرکز مطالعات سنجش از دور وGIS ، دانشکدة علوم زمین، دانشگاه شهید بهشتی

3 مرکز مطالعات سنجش از دور وGIS ، دانشکدة علوم زمین، دانشگاه شهید بهشتی

چکیده

شاخص‌های طیفی پوشش گیاهی به‌منزلة ابزاری مناسب برای تخمین میزان تولید محصولات کشاورزی استفاده می‌شوند. بااین‌حال، تعداد محدود تصاویر از عوامل اصلی کاهش کارآیی شاخص‌ها به‌منظور تخمین تولید شمرده می‌شود. از سوی دیگر، ارزیابی توانایی شاخص‌ها در تخمین تولید از راه ترکیب داده‌های مادیس و لندست، در مواردی که تعداد داده‌های لندست کم باشد، کمتر مورد توجه قرار گرفته است. هدف تحقیق حاضر، در گام نخست، معرفی شاخص‌ها یا شاخص منتخب در تخمین تولید کلزا و در گام بعدی، استفاده از تکنیک‌های تلفیق داده برای افزایش کارآیی شاخص منتخب است. کلزا ازجمله محصولات کشاورزی است که، به‌دلیل گل‌دهی در دورة رشد، ویژگی‌های طیفی خاصی دارد. در این تحقیق، پایگاه داده‌ای از میزان تولید محصول کلزا و سری زمانی داده‌های لندست و مادیس کشت‌و‌صنعت مغان تهیه و سپس ده شاخص متفاوت به‌قصد تخمین تولید کلزا ارزیابی شد. در ادامه، رابطة میزان تولید با شاخص پیشنهادی بررسی و مشخص شد که شاخصNDYI ، در طول زمان گل‌دهی، دقتی بیشتر از سایر شاخص‌ها دارد (r = 0.73). با تلفیق داده‌های سری زمانی لندست و مادیس مبتنی‌بر الگوریتم مدل تطبیقی ادغام بازتابندگی مکانی و زمانی بهبودیافته (ESTARFM)، همبستگی و RMSE (kg/ha) به‌ترتیب 7% و 0.11 افزایش و کاهش یافت. تحقیق حاضر نشان داد که استفاده از تکنیک‌های تلفیق داده امکانِ افزایش کارآیی شاخص‌های طیفی را به‌منظور تخمین تولید محصول فراهم می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

Estimating Yield of Canola Based on Time Series of Remote Sensing Data

نویسندگان [English]

  • Davoud Ashourloo 1
  • Hamid Salehi Shahrabi 2
  • Hamed Nematollahi 3
1 Assistant Prof. of R.S. & GIS Research Center, Shahid Beheshti University
2 Ph.D. Student of R.S. & GIS Research Center, Shahid Beheshti University
3 R.S. & GIS Research Center, Shahid Beheshti University
چکیده [English]

Spectral vegetation indices have been used as a useful tool in remote sensing to estimate the yield of agricultural crops. However, one factor, which reduces the capability of indices for crop yield estimation, is the limited number of available satellite images. Furthermore, in cases when there are not enough Landsat images, the capabilities of spectral indices in yield estimation using a fusion of MODIS and Landsat data, have been less investigated. The aim of this paper is, first, to introduce the most efficient index/indices for estimating the canola yield and, second, to try to use data fusion techniques in order to increase the efficiency of the selected index/indices. Due to flowering in the growth period, canola has special spectral features. In this research, to estimate the yield of canola, a yield database along with the time series of the Landsat and MODIS data of Moghan Agro-Industry Company fields were provided. Then, 10 spectral indices were evaluated for estimating the canola yield. The relations between the canola yield and the candidate indices were investigated and it was revealed that, during the flowering period, the NDYI index obtained a higher accuracy compared with other indices (r = 0.73). The fusion of the Landsat and MODIS time series data based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), resulted in a 7%-increase and an 11%-decrease in correlation and RMSE (kg/ha), respectively. This research indicated that data fusion techniques are able to improve the performance of spectral indices and hence increase the accuracy of crop yield estimation.

کلیدواژه‌ها [English]

  • Agriculture
  • Spectral indices
  • Yield estimation
  • Canola
  • Data fusion
  • MODIS and Landsat

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