برآورد پارامترهای روزانه و ماهانه دمای هوا در استان کردستان با بکارگیری تصاویر سنجنده MODIS

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه کردستان

2 دانش آموخته کارشناسی ارشد آبیاری و زهکشی، دانشگاه کردستان

3 دانشجوی دکتری هواشناسی کشاورزی دانشگاه کلگری کانادا

چکیده

هدف از انجام این تحقیق، برآورد توزیع مکانی سه پارامتر دمای هوا شامل دمای کمینه، دمای بیشینه و دمای میانگین در مقیاس­های زمانی روزانه و ماهانه در استان کردستان با بکارگیری تصاویر سنجنده MODIS نصب شده بر روی ماهواره­های Aqua و Terra است. برای این منظور، 8 ایستگاه سینوپتیک در استان کردستان انتخاب شدند و برای سال­های 2013 و 2014 در این 8 ایستگاه، داده­های روزانه کمینه، بیشینه و میانگین دمای هوا و همچنین دمای سطح زمین در 4 زمان مختلف شبانه­روز با انجام پردازش­هایی بر روی تصاویر سنجنده MODIS استخراج شدند. سپس از روش رگرسیون خطی چند متغیره جهت استخراج روابط رگرسیونی بین هر کدام از سه پارامتر دمایی مذکور با دماهای سطح زمین برای کل استان کردستان و جهت سنجش خطا از روش اعتبارسنجی متقابل با بکارگیری سه شاخص میانگین قدر مطلق خطا، میانگین اریبی خطا و ضریب کارایی نش - ساتکلیف استفاده شد. نتایج نشان داد که ارتباطی قوی بین هر سه پارامتر دمایی با دماهای سطح زمین مستخرج از تصاویر ماهواره­ای وجود دارد. نتایج حاصل از اعتبارسنجی متقابل نیز حاکی از تطابق مناسب و قابل قبول بین مقادیر اندازه­گیری­شده سه پارامتر دمایی مذکور با مقادیر برآورد شده توسط روابط رگرسیونی در هر دو مقیاس زمانی روزانه و ماهانه بود به گونه­ای که شاخص میانگین قدر مطلق خطا برای سه پارامتر دمای کمینه، دمای بیشینه و دمای میانگین در مقیاس روزانه به ترتیب با 7/2، 1/2 و 6/1 درجه سانتیگراد و در مقیاس ماهانه به ترتیب 9/1، 1/2 و 1/1 درجه سانتیگراد بدست آمد که نشان­دهنده آن است که مقادیر این پارامترهای دمایی را در نقاطی از استان کردستان که فاقد ایستگاه هواشناسی هستند می­توان با استخراج دماهای سطح زمین از سنجنده MODIS برای این نقاط و بکارگیری این روابط رگرسیونی با دقت مناسب برآورد کرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimating daily and monthly air temperature parameters at Kurdistan province using MODIS sensor images

نویسندگان [English]

  • Younes Khoshkhoo 1
  • Sorour Esmaeili 2
  • Masoud Abdollahi 3
1 Water science and Engineering Department, Agriculture Faculty, University of Kurdistan
2 University of Kurdistan
3 Ph.D. student
چکیده [English]

The object of this research is the estimation of spatial distribution of three air temperature parameters at daily and monthly scales including minimum, maximum and mean temperatures at the Kurdistan province using MODIS sensor images setted on Aqua and Terra satellites. For this object, 8 synoptic stations at Kurdistan province were selected and for these 8 stations at 2013 and 2014, daily minimum, maximum and mean air temperature data and also land surface temperature at 4 daily times at these 8 stations for 2013 and 2014 years were extracted by processing on the MODIS sensor images. Afterwards, the multiple linear regression method was used to extract regional regression models for Kurdistan province between these 3 air temperature parameters and land surface temperature and to assesing the errors, cross validation based on the Mean Absolute Error, Mean Bias Error and Nash-Sutcliffe Efficiency coefficient was adopted. The results showed that it is a powerful relation between all of these 3 air temperture and land surface temperatures extracted by sattelite images. The results of cross validation showed an approperiate and reseanable agreement between the measured and estimated values of these 3 parameters at both daily and monthly scales so that the mean absolute error for minimum, maximum and mean temperatures were 2.7, 2.1 and 1.6 °C at daily scale and 1.9, 2. 1 and 1.1 °C at monthly scale, respectively. These results showed that it is possible to estimate these air temperature parameters at the places witout any meteorological stations with an approperiate and acceptable accuracy by extracting land surface temperatures of MODIS sensor for these places and applying the extracted regression models.

کلیدواژه‌ها [English]

  • Air Temperature
  • Kurdistan province
  • Land surface temperature
  • MODIS
  • Multiple Linear Regression
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