بهبود قدرت تفکیک زمانی-مکانی داده های تبخیر-تعرق واقعی با استفاده از ترکیب داده های مادیس و لندست-8

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد / مهندسی آبیاری و زهکشی، شرکت آب منطقه‌ای تهران.

2 استادیار / گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تربیت مدرس.

3 دانشیار / گروه مهندسی آبیاری و زهکشی، دانشگاه تربیت مدرس.

چکیده

هدف این مطالعه تولید نقشه‌های تبخیر- تعرق روزانه با قدرت تفکیک مکانی 30 متر و به صورت روزانه برای اراضی کشت و صنعت امیرکبیر با استفاده از دو سناریو می باشد. در سناریوی اول پارامترهای ورودی مورد نیاز الگوریتم سبال حاصل از تصاویر مادیس به قدرت تفکیک مکانی لندست-8 ریزمقیاس شد، سپس با استفاده از الگوریتم سبال و پارامترهای ورودی ریزمقیاس شده، تبخیر- تعرق واقعی محاسبه شد. در سناریوی دوم تبخیر- تعرق واقعی‌ بدست آمده از سنجنده مادیس به قدرت تفکیک مکانی لندست-8 ریزمقیاس شد. در سناریوهای اول و دوم ریزمقیاس کردن داده‌ها با سه روش نسبت، رگرسیون و شبکه‌ عصبی مصنوعی و با دو رویکرد مختلف انجام شد. در رویکرد اول فاصله زمانی بین تصویر پایه و تصویر ریزمقیاس شده از 1 روز تا 15 روز متغیر می‌باشد ولی در رویکرد دوم فاصله زمانی بین دو تصویر پایه و ریزمقیاس شده 1 روز می‌باشد. با مقایسه تبخیر- تعرق‌های واقعی ریزمقیاس شده با مقادیر تبخیر- تعرق واقعی بدست آمده از تصویر لندست-8 روش رگرسیون در سناریوی دوم با رویکرد اول با مجذور میانگین مربعات خطا 87/0 میلیمتر در روز دارای بهترین نتیجه و روش شبکه عصبی مصنوعی در سناریوی دوم با رویکرد دوم با مجذور میانگین مربعات خطا 25/2 میلیمتر در روز دارای بدترین نتیجه بود. اگرچه نتایج محاسبه تبخیر-تعرق واقعی حاصل از ریزمقیاس نمایی در تمامی روشها در هر دو سناریو و با هر دو رویکرد، نسبت به تبخیر- تعرق واقعی بدست آمده از تصویر مادیس با مجذور میانگین مربعات خطا 19/3 میلیمتر در روز دارای صحت بهتری بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Spatio-temporal resolution improvement of actual evapotranspiration using MODIS and Landsat-8 data fusion

نویسندگان [English]

  • Hamid Salehi 1
  • Ali Shamsoddini 2
  • Seyed Majid Mirlatifi 3
1 Master of Agricultural Engineering (Irrigation), Regional Water Company of Tehran.
2 Assistant professor, Department of Remote Sensing and GIS, Tarbiat Modares University.
3 Associate professor, Department of Irrigation and Drainage, Tarbiat Modares University.
چکیده [English]

Recently, downscaling algorithms have been developed to obtain ET images with high temporal-spatial resolution. The purpose of the present study is to produce daily ET maps with spatial resolution of 30 m for farmlands of Amirkabir Agriculture & Industry. To reach this goal, two different scenarios were used. In the first scenario, SEBAL algorithm input parameters (surface albedo coefficient, normalized difference vegetation index [NDVI], leaf area index [LAI] and land surface temperature [LST]) calculated from MODIS data were downscaled to spatial resolution of Landsat-8, and then actual ET was calculated. In the second scenario, ET data estimated by MODIS data and SEBAL algorithm was downscaled to Landsat-8 spatial resolution. In the first and second scenarios, downscaling was conducted by applying three methods including ratio, regression and neural network. Also, two approaches were applied in this study. In the first approach, the time lag between the base image (image with higher spatial resolution) and MODIS image varies from 1 to 15 days, whereas in the second approach the time lag was 1 day. Comparing downscaled actual ET with actual ET calculated from Landsat-8 data, the regression method applied in the second scenario and first approach indicated the best result with RMSE=0.87 mm/day and neural network used in the second scenario and second approach showed the worst result with RMSE=2.25 mm/day. However downscaled actual ETs derived from different methods were more accurate than actual ET resulted from MODIS data with RMSE= 3.19 mm/day.

کلیدواژه‌ها [English]

  • Evapotranspiration
  • MODIS
  • Landsat-8
  • Downscaling
  • SEBAL
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