واکاوی وردش‌های مکانی دما در حوضۀ زاینده‌رود به کمک سنجندۀ مودیس

نوع مقاله : مقاله کامل

نویسندگان

1 دانشیار، گروه جغرافیا، دانشگاه پیام نور، تهران، ایران

2 دانشجوی دکتری آب‌وهواشناسی، دانشگاه اصفهان

چکیده

هدف از پژوهش کنونی بررسی وردش‏های مکانی دما در حوضة زاینده‏رود است. بدین منظور، داده‏های دمای رویة خاک سنجندة مودیس تررا برای بازة زمانی 1379 ـ 1393 به‌کار گرفته شد. داده‏های دمای این سنجنده در تفکیک‏های مکانی گوناگونی در دسترس است، اما در این پژوهش از خردترین گونة دادة این فرآوردة دورسنجی، که در تفکیک مکانی 1×1 کیلومتری در دسترس است، بهره گرفته شد. پس از استخراج داده‏ها بر روی حوضة زاینده‏رود، شیب خط دما روی 48347 یاختة مورد بررسی در حوضه محاسبه شد. اما، برای اطمینان از روند، بازة اطمینان رگرسیونی بر روی یاخته‏ها به‌کار بسته شد و فقط یاخته‏هایی که از دیدگاه علامت شیبِ خط هم‌راستا بودند استخراج و به نمایش گذاشته شدند. یافته‏های این پژوهش نشان داد در همة فصول در حوضه روندهای افزایشی و کاهشی دمای رویه دیده می‏شود. بیشترین گسترة روند افزایشی دمای رویه در فصل زمستان و در بخش‏های غربی حوضه دیده شد. همچنین، آشکار شد در فصل زمستان همبستگی بسیاری (بیش از 90/0) میان میزان افزایش دما و ارتفاع دیده می‏شود و با افزایش ارتفاع بر میزان روند نیز افزوده می‏شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analysis of spatial variation of land surface temperature over Zayanderoud River Basin based on MODIS sensor

نویسندگان [English]

  • Amir Hossein Halabian 1
  • Mohamad Sadegh Keykhosravi Kiany 2
1 Associate Professor, Geography Department, Payame Noor University, Tehran, Iran
2 Ph.D. Candidate in Climatology, Isfahan University, Iran
چکیده [English]

Introduction
Soil temperature and its changes both in space and time is one of the most important factors that not only affect matter and energy transfer in soil but also influence the direction and amount of all physical processes in soil. Soil temperature depends on several factors including topography, sun radiation, air temperature, amount of soil moisture, the thermal properties such as heat capacity, coefficient of thermal conduction and specific heat. By the advent of satellite measurements in the last decades, there have been some studies focusing on the use and validation of remote sensing measurement of land surface temperature data mainly MODIS products. MODIS land surface data have been validated over different land types like lake (Want et al., 2002; Hook et al., 2004; Wan, 2008) and rice (Want et al., 2004; Galve et al., 2007).
 
Materials and Methods
In this research, the land surface temperature data of MODIS Terra in the spatial resolution of 1×1 km was exploited from NASA web site. These kinds of data are available in sinusoidal projection system. To investigate the role of elevation on land surface temperature, the digital elevation model (Dem) of the region was also obtained from NASA web site. Terra data are available from the year 2000 to 2015. These data are available in hdf format. As the first step only the pixels that felled into the Zayanderoud River Basin were selected from the data. This extraction was done using the inpolygon function in Matlab. By applying this function in Matlab there were exactly 48347 pixels that were the corner stone of our judgment about land surface temperature. In the second step, the daily matrix of temperature was constructed for each calendar day and based on the daily time series of the seasonal data were based on the daily data. Then, we calculated the rate of trend over each of the pixels for each season by applying regression equation on each of the 48347 pixels.
Results and Discussion
Analysis of trend in land surface temperature for each of the seasons indicated that there has been trend whether positive or negative in each of the seasons. The examination of trend for the season of spring showed that most of the territories of the Basin have experienced a negative trend but in central parts of the Basin a very marked positive trend dominates this region. The investigations for the summer season indicated that in many parts of the Basin negative trend has occurred but in the central parts of the Basin a distinguished positive trend can be seen. The analysis for the fall season showed that in this time many parts of the Basin like the other seasons have had a downward trend in the land surface temperature. But similar to the other seasons, central parts of the Basin have experienced a positive trend. The analysis for the winter season showed that in this period the areas that have had negative trend are less than the areas that have had positive trend in extent. The surprising note about this season is that there is a positive and direct relation between the rate of trend and elevation. Greater is the elevations, the greater the rate of trend. This means that in this season the highlands of the western parts of the Basin that are basically regarded as the valuable source for keeping snow covers and snow glaciers can’t have snow covers like before, due to the dominating positive trend occurring in these regions.
 
Conclusion
In this study, the daily time series of MODIS Terra was applied to explore the spatial trend of land surface temperature over Zayanderoud River Basin. The smallest resolution of 1×1 km of these data was used to get a better picture of the trend. As the first step, only the pixels that fell into the natural border of the Basin were extracted by using Matlab functions. In the next step, the daily values of land surface temperature were aggregated to monthly and seasonal time scales. The analysis of the trend for each of the seasons indicated that in all of the seasons positive and negative trends have occurred but in general negative trend has had a greater domination. In the spring season only central parts of the basin has represented a positive trend but in the other parts decreasing trend prevailed. In the summer and fall like spring only the central parts of the Basin have had a positive trend. But in the winter the highland of western parts of the Basin has had a very marked positive trend that can be hazardous for this region due to this fact that these mountainous regions of the Basin is regarded as the source of snow cover of the Basin. 

کلیدواژه‌ها [English]

  • Land surface temperature
  • MODIS Terra
  • Spatial variations
  • Zayandroud River Basin
Atlas, R.; Wolfson, N. and Terry, J. (1993). The effect of SST and soil moisture anomalieson GLA model simulations of the 1988 U.S. summer drought, Journal of Climate, 6: 2034-2048.
Coll, C.; Caselles, V.; Galve, J.M.; Valor, E.; Niclos, R.; Sanchez, J.M. and Rivas (2005). Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data, Remote Sens. Environ., 97: 288-300.
Coll, C.; Wan, Z. and Galvem, J.M. (2009). Temperature-basedand radiance- based validations of the V5 MODIS land surfacetemperature product, J. Geophys. Res., 114: D20102.
Crosman, E.T. and Horel, J.D. (2009). MODIS-derived surface temperatureof the Great Salt Lake, Remote Sens. Environ., 113: 73-81.
Dai, A.; Trenberth, K.E. and Karl, T.R. (1999). Effects of clouds, soil moisture, precipitation,and water vapor on diurnal temperature range, Journal of Climate, 12: 2451-2473.
Galve, J.M.; Coll, C.; Caselles, V.; Valor, E.; Niclos, R.; Sanchez, J.M. and Mira, M. (2007). Simulation and validation of land surface temperature algorithms for MODIS and AATSR data, Tethys, 4: 27-32.
Hook, S.J.; Vaughan, R.G.; Tonooka, H. and Schladow, S.G. (2007). Absolute radiometric inflight validation of mid infraredand thermal infrared data from ASTER and MODIS on theTerra Spacecraft using the Lake Tahoe, CA/NV, USA, automatedvalidation site, IEEE Geosci. Remote S., 45: 1798-1807.
Jin, M. (2000). Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle 2. Cloudy-pixel treatment, Journal of Geophysical Research, 105: 4061-4076.
Jin, M. (2004). Analysis of land skin temperature using AVHRR observations, American Mete-orological Society, 85: 587-600.
Jin, M. and Dickinson, R.E. (1999). Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle 1. Without clouds, Journal of Geophysical Research, 104: 2105-2116.
Jin, M. and Dickinson, R.E. (2010). Land surface skin temperature climatology: benefitting from the strengths of satellite observations, Environ Res Lett, 5: 044004.
Langer, M.; Westermann, S. and Boike, J. (2010). Spatial and temporalvariations of summer surface temperatures of wet polygonaltundra in Siberia – implications for MODIS LST based permafrostmonitoring, Remote Sens. Environ., 114: 2059-2069.
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.
Price, J. (1984). Land surface temperaturemeasurements fromthe split window channelsof the NOAA 7 advanced very high resolution radiometer, Journal of Geophysical Research, 89: 7231-7237.
Salama, M.; Velde, L.; Zhong, L.; Ma, Y.; Ofwono, M. and Su, Z. (2012). Decadal variations of land surface temperature anomalies observed over the Tibetan Plateau by the Special Sensor Microwave Imager (SSM/I) from 1987 to 2008, Climatic Change, 114: 769-781.
Segal, M.; Garratt, J.R.; Kallos, G. and Pielke, R.A. (1989). The impact of wet soil and canopytemperatures on daytime boundary-layer growth, Journal of Atmospheric Science, 46: 3673-3684.
Sommers, L.E.; Gilmour, C.M.; Wildung, R.E. and Beck, S.M. (1981). The effect of water potential ondecomposition processes in soils, in Water Potential Relations in Soil Microbiology, 9: 97-117.
Song, J-H; Kang, H-S; Byun, Y-H and Hong, S-Y (2009). Effects of the Tibetan Plateau on the Asian summer monsoon: a numerical case study using a regional climate model, Int J. Climatol.
Trenberth, K. et al. (2007). Observations: surface and atmospheric climate change, In: Climate change 2007: The physical science basis, Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA.
Wan, Z. (2008). New refinements and validation of the MODIS landsurfacetemperature/emissivity products, Remote Sens. Environ., 112: 59-74.
Wan, Z. and  Dozier, J. (1996). A generalized split-window algorithm for retrieving land surfacetemperature from space, IEEE Transactions on Geoscience and Remote Sensing, 34: 892-905.
Wan, Z.; Zhang, Y.; Zhang, Q. and Li, Z.-L. (2004). Qualityassessment and validation of the MODIS global landsurface temperature, Int. J. Remote Sens., 25: 261-274.
Westermann, S.; Langer, M. and Boike, J. (2011). Spatial and temporalvariations of summer surface temperatures of high-arctic tundraon Svalbard – Implications for MODIS LST based permafrostmonitoring, Remote Sens. Environ., 115: 908-922.
Wu, GX; Liu, YM; Wang, TM; Wan, R.J.; Liu, X; Li, WP; Wang, ZZ; Zhang, Q.; Duan, A.M. and Liang, X.Y. (2007). The influence of mechanical and thermal forcing by the Tibetan Plateau on Asian climate, J. Hydrometeorol, 8: 770-789.