بررسی شیب دمای سطح زمین در ایران با داده های روزهنگام مودیس

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 دانشجوی دورة دکتری تخصصی آب‌وهواشناسی، دانشگاه محقق اردبیلی

2 دکتری تخصصی اقلیم‌شناسی، دانشیار گروه جغرافیای طبیعی دانشگاه محقق اردبیلی

3 دکتری تخصصی اقلیم‌شناسی، استاد گروه جغرافیای طبیعی دانشگاه اصفهان

چکیده

آگاهی از دمای سطح زمین تغییر‏ات زمانی‌- مکانی ترازمندی انرژی در سطح زمین را‏ آشکار می‏سازد. در داده‏های دمای سطح زمین مودیس اختلاف زمان خورشیدی محلی وجود دارد. این اختلاف ممکن است به دلیل تفاوت زمانی در برداشت پیکسل‏های یک خط پیمایش ماهواره در یک روز باشد یا در روزهای مختلف زمان محلی برداشت دما در یک پیکسل متغیر باشد. هدف از پژوهش کنونی بررسی شیب دمای سطح زمین و تغییرات زمانی‌- مکانی آن در ایران است که با داده‏های روزهنگام مودیس تِررا و آکوا بررسی شده است. از نتایج این پژوهش می‏توان در برآورد دمای سطح زمین برای یک ساعت محلی ثابت استفاده کرد. بدین ترتیب، امکان مقایسة داده‏های دورسنجی دمای سطح زمین با داده‏های ایستگاهی و نیز امکان مقایسة دمای پیکسل‏های مختلف در سراسر ایران با یکدیگر فراهم می‏آید. تغییرات زمانی‌- مکانی چشم‏گیری در شیب دمای سطح زمین ایران دیده می‏شود؛ این تغییرات از شرایط محیطی و تغییرات دریافت انرژی خورشید اثر می‏پذیرد. در ماه‏های مختلف سال شیب‏های دمایی صفر تا 1+ درجة کلوین بر ساعت و 1+ تا 2+ درجة کلوین بر ساعت گسترة بیشتری از ایران را پوشش می‏دهند؛ با این حال، در دورة سرد سال شیب‏های صفر تا ۱- درجه‏، به‌ویژه در بلندی‏های البرز و زاگرس، گسترش می‏یابد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analysis of land surface temperature gradient of Iran using MODIS Terra and Aqua data

نویسندگان [English]

  • Masood Moradi 1
  • Boroumand Salahi 2
  • Seyyed Abolfazl Masoodian 3
1 Ph.D. Candidate in Climatology, University of Mohaghegh Ardabili, Ardabil, Iran
2 Associate Professor, Department of Physical Geography, University of Mohaghegh Ardabili, Ardabil, Iran
3 Ph.D. in Climatology, Department of Physical Geography, University of Isfahan, Isfahan, Iran
چکیده [English]

Introduction
The MODIS facility for the Earth Observing System is a key element that supports ambitious goals related to studying the Earth as a system. One of the MODIS products is high quality land surface temperature data produced in Terra and Aqua platforms. Knowledge of the LST provides information on the temporal and spatial variations of the surface equilibrium state and is of fundamental importance in many applications. Therefore, it is required to conduct a wide variety of climatic, hydrological, ecological, and biogeochemical studies. Due to the intrinsic scanning characteristics of the MODIS instrument onboard the polar-orbiting satellites, the differences in local solar time for pixels along a given scan line on the same day or for the same pixel on different days in one revisit period can be detected for 2 hours. As LST changes with local solar time, it is not possible to directly compare the LST of different pixels in the same day or of the same pixels in different days. Awareness about slope of land surface temperature is an important factor for cognition of land surface temperature behavior that can be used for increasing spatial and temporal resolution, comparability with other data, and accuracy achievement. The results will help calculate a time consistent land surface temperature.
 
Materials and Methods
Land surface temperature data used in this research are produced using Normalized split windows algorithm. These data haver been downloaded from MODIS website (http://reverb.echo.nasa.gov/reverb) in daily time scale for temporal range of 2002/07/08–2015/11/30. Prior to this date, Aqua MODIS data were not available. This dataset contain measurements of land surface temperature, quality assurance, view time, view angel, land surface emissivity available for day time and night time. MODIS Terra 10:30AM and Aqua 1:30PM data have been used in this study. A matrix in dimensions of 4984*1884077 is provided from this dataset for Terra and Aqua day time land surface temperature and day view time. The matrix has provided basic information for this study. The slope of land surface temperature between two observation of Terra (10:30AM) and Aqua (1:30PM) is calculated. View time and land surface temperature measurements should initially be recalled and calculate variation of land surface temperature in relation to observation time distance between Terra/Aqua measurement for each pixel of Iran for every day of time series. The land surface temperature gradient has been calculated in monthly long term mean for spatial and temporal analysis.
 
Results and Discussion
View time is an important parameter in analysis of land surface temperature gradient. View time distribution of land surface temperature in Iran show that Terra and Aqua view time has a high uniformity in frequency percent of observation times for land surface temperature at 10-12 and 12:30-14:30 for Terra and Aqua, respectively. The coordination of land surface temperature gradient and topography in Iran is high. Low elevation lands between Zagros and central mountain ranges with northwest to southeast direction and central hollows such as Kavir plain, Lut desert and Jazmouryan is visible with higher slope in land surface temperature in all different months of year. North and south shorelines and high elevations are the regions with smaller gradient. The gradient of less than 2 kelvin/hour in land surface temperature occurred in 85% of Iran territory. Difference between Terra and Aqua land surface temperature decreased clockwise from northeast aspects to southwest and increased from northwest to northeast. The gradient of land surface temperature has an inverse relation with slope of land and decreased one kelvin/hour from zero to 22 degree in slope. 
 
Conclusion
Significant spatial and temporal variation that occurred in land surface temperature gradient of Iran is the result of variation in environmental conditions and incoming solar radiation. The gradient of land surface temperature in shorelines and mountainous regions is lower than deserts and low level elevations in all months of year. The gradient of zero to ±1 and +1 to +2 kelvin/hour in land surface temperature has covered great areas of Iran in different months. Zero to -1 kelvin/hour gradient spread can be seen in winter season in high level elevations of Alborz and Zagros mountains. Decrease in land surface temperature gradient with increase land slope can reflect relation between land surface temperature and elevation. The results show that small amounts of land surface temperature gradient are the characteristic of major parts of Iran. In these conditions we can produce a time consistent land surface temperature data for each pixel of every day in available time series for a time between MODIS Terra and Aqua observations.
 

کلیدواژه‌ها [English]

  • Iran
  • land surface temperature gradient
  • MODIS
  • temporal and spatial variations
Aires, F.; Prigent, C.; Rossow, W.B. (2004). Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions, Journal of geophysical research, 109: D04313.
Coccia, G.; Siemann, A.L. and Wood, M.P., E.F. (2015). Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS, Remote Sensing of Environment, 170: 290-305.
Duan, S.-B.; Wu, H.; Wang, N.; Zhou, X.-M.; Tang, B.-H. and Li, Z.-L. (2011). preliminary results of temporal normalization of modis land surface temperature, IEEE, IGARSS conference, pp. 297-300.
Duan, S.-B.; Li, Z.-L.; Wang, N.; Wu, H. and Tang, B.-H. (2012). Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data, Remote Sensing of Environment, 124: 15-25.
Duan, S.-B.; Li, Z.-L.; Tanga, B.-H.; Wu, H. and Tang, R. (2014). Generation of a time-consistent land surface temperature product from MODIS data, Remote Sensing of Environment, 140: 339-349.
Gottsche, F.M. and Olesen, F.S. (2001). Modeling of diurnal cycle of brightness temperature of extracted from METEOAT data, Remote sensing of Environment, 76: 337-348.
Ignatov, A. and Gutman, G. (1998). Diurnal cycles of land surface temperatures, Advanced Space Research, 22: 641-644.
Hulley, G.C. and Hook, S.J. (2010). Generating Consistent Land Surface Temperature and Emissivity Products Between ASTER and MODIS Data for Earth Science Research, IEEE Transactions on Geoscience and Remote Sensing, 49(4): 1304-1315.
Inamdar, A.K.; French, A.; Hook, S.; Vaughan, G.; Luckett, W. (2008). Land surface temperature retrieval at highspatial and temporal resolutions over the southwestern United States, Jurenal of Geophysics Research, 113: D07107.
Jin, M. and Dickinson, R.E. (1999). Interpolation of surface radiative temperature measuredfrom polar orbiting satellites to a diurnal cycle 1. Without clouds, Journal of Geophysical Research, 104(D2): 2105-2116.
Li, Z.L.; Tang, B.-H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Trigo, I.F. and Sobrino, J.A. (2013). Satellite-derived land surface temperature: Current status and perspectives, Remote Sensing of Environment, 131: 14-37.
Liu, H. and Weng, Q. (2012). Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile Virus outbreak in Los Angeles in 2007, Remote Sensing of Environment, 117: 57-71.
Oku, Y.; Ishikawa, H.; Haginoya, S. and Ma, Y. (2006). Recent trends in land surface temperature on the tibetan plateau, Journal of Climate, 19: 2995-3003.
Quan, J.; Chen, Y.; Zhan, W.; Wang, J.; Voogt, J. and Li, J. (2014). A hybrid method combining neighborhood information from satellite data with modeled diurnal temperature cycles over consecutive days, Remote Sensing of Environment, 155: 257-274.
Salomonson, V.V.; Barnes, W.L.; Maymon, P.W.; Montgomery, H.E. and Ostrow, H. (1989). MODIS: Advanced Facility Instrument for Studies ofthe Earth as a System, Transactions on Geoscience and Remote Sensing, 27(2): 145-153.
Schadlich, S.; Gottsche, F.M. and Olesen, F.-S. (2001). Influence of Land Surface Parameters and Atmosphere on METEOSAT Brightness Temperatures and Generation of Land Surface Temperature Maps by Temporally and Spatially Interpolating Atmospheric Correction, Remote Sensing of Environment, 75: 39-46.
Sun, D.; Kafatos, M.; Pinker, R.T. and Easterling, D.R. (2006). Seasonal Variations in Diurnal Temperature Range From Satellites and Surface Observations, Transactions on Geoscience and Remote Sensing, 44(10): 2779-2785.
Wan, Z. and Dozier, J. (1996).A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE Transactions on Geoscience and Remote Sensing, 34: 892-905.
Wan, Z. and Li, Z.-L. (1997). A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data, IEEE Transactions on Geoscience and Remote Sensing, 35: 980-996.
Wan, Z. and Li, Z.-L. (2008). Radiance-based validation of the V5 MODIS land-surface temperature product, International Journal of Remote Sensing, 29(17): 5373-5395.
Wu, P.; Shen, H.; Zhang, L. and Göttsche, F.-M. (2015). Integrated fusion of multi-scale polar orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature, Remote Sensing of Environment, 156: 169-181.
Zakšek, K. and Oštir, K. (2012). Downscaling land surface temperature for urban heat island diurnal cycle analysis, Remote Sensing of Environment, 117: 114-124.
Zhan, W.; Chen, Y.; Voogt, J.; Zhou, J.; Wang, J.; Liu, W. and Mad, W. (2012). Interpolating diurnal surface temperatures of an urban facet using sporadic thermal observations, Building and Environment, 57: 239-252.