تخمین دمای هوای بیشینه و کمینة روزانه با استفاده از محصولات دمای سطح زمین سنجندة مادیس

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد گروه مهندسی آبخیزداری، دانشکدة منابع‌ طبیعی و علوم دریایی، دانشگاه تربیت مدرس، نور، مازندران

2 استاد گروه مهندسی آبخیزداری، دانشکدة منابع ‌طبیعی و علوم دریایی، دانشگاه تربیت مدرس، نور، مازندران

3 استادیار گروه مهندسی آبخیزداری، دانشکدة منابع ‌طبیعی و علوم دریایی، دانشگاه تربیت مدرس، نور، مازندران

چکیده

با توجه به اهمیت داده‌های هواشناسی و وجود محدودیت‌ داده‌برداری در ایستگاه‌های زمینی، فنّ سنجش از دور می‌تواند نقش مهمی در تهیة این داده‌ها ایفا کند. هدف از این پژوهش ارزیابی کمّی دمای سطح زمین (LST) حاصل از تصاویر سنجندة مادیس، برای تخمین دمای هوای بیشینه و کمینة روزانه در حوضة ‌آبخیز طالقان است. برای این منظور، داده‌های دمای هوای بیشینه و کمینة روزانة سه ایستگاه هواشناسی زمینی و مقادیر LST روز و شب و NDVI سنجندة مادیس، متعلق به دورة زمانی 2009 تا 2015، دریافت و تهیه شد. سپس با استفاده از روش رگرسیون خطی چندگانه، بین هریک از متغیرهای مؤثر و دمای هوای بیشینه و کمینة روزانه در ایستگاه‌های زمینی، ارتباط برقرار شد. نتایج نشان داد که بین دمای هوای بیشینه و کمینة روزانه در ایستگاه‌های زمینی، با LST روز و شب و NDVI حاصل از سنجندة مادیس، همبستگی معنی‌داری وجود دارد؛ بنابراین از این متغیر‌ها در روابط رگرسیونی استفاده شد. نتایج حاصل از اعتبارسنجی نشان داد روابطی که با همة متغیرهای مؤثر ایجاد شده است بیشترین صحت را دارد؛ به‌طوری‌که بهترین مدل در تخمین بیشینة دمای هوای روزانه، دارای مقادیر ، NSE و RMSE، به‌ترتیب 75/0، 75/0 وC ˚ 7/4+ و درمورد کمینة دمای هوای روزانه، به‌ترتیب 71/0، 71/0 و  C˚ 9/2+ است؛ ازاین‌رو می‌توان با تبدیل دمای سطح زمین حاصل از تصاویر سنجندة مادیس، دمای هوا را با دقت بالا در مقیاس روزانه و ماهیانه، برای استفاده در پژوهش‌های گوناگون، تخمین زد.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of Maximum and Minimum Daily Air Temperature Using MODIS Surface Temperature Products

نویسندگان [English]

  • Mohammad Tavosi 1
  • Mehdi Vafakhah 2
  • Vahdi Moosavi 3
1 M.Sc. Student, Dep. of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modarres University, Noor, Mazandaran
2 Prof. of Dep. of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modarres University, Noor, Mazandaran
3 Assistant Prof., Dep. of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modarres University, Noor, Mazandaran
چکیده [English]

Due to the importance of meteorological data and limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the Land Surface Temperature (LST) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images for estimating the maximum and minimum daily air temperature in the Taleghan watershed. For this purpose, the maximum and minimum daily air temperature data of three existing ground stations for the period 2009 to 2015 were obtained. Day and night LST and Normalized Difference Vegetation Index (NDVI) values ​​of MODIS were also prepared. The relationships between each of the effective variables and maximum and minimum daily air temperature in ground stations have been extracted using multiple linear regression method. The results showed that there was a significant correlation between maximum and minimum daily temperature of ground stations with day and night LST and NDVI from MODIS sensor. Therefore, these variables were used in regression relationships. The results of validation showed that the established relationships with all effective variables had the most accuracy. Therefore, the best model for estimating the maximum daily air temperature had , NSE and RMSE values ​​of 0.74, 0.74, and +4.7, respectively and for estimating the minimum daily air temperature had 0.71, 0.72 and +2.9, respectively. Therefore, by converting the surface temperature obtained from MODIS sensor images, the air temperature can be estimated with high accuracy on a daily and monthly scales for various studies.

کلیدواژه‌ها [English]

  • Air temperature
  • Aqua and Terra satellites
  • Land Surface Temperature
  • Vegetation index

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