Analysis of daytime land surface temperature in Iran based on the MODIS sensor output

Document Type : Original Article

Authors

1 Department of Climatology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

2 Department of Climatology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran

Abstract

Introduction:
The air temperature parameter is one of the most important measures for identifying the climatic and environmental conditions of each region. Today, by using thermal infrared data, LST maps can be prepared without physical contact with objects or surfaces. Awareness of the spatial and temporal distribution of LST is essential to determine the land energy balance, the evapotranspiration and meteorology studies is essential. LST is a function of pure energy at the land surface which depends on the amount of energy reaching the land surface, surface emissivity, humidity, and air flow. The present study intends to investigate the state of Daytime LST in Iran in different months of the year based on the output of MODIS Terra images.Materials and methods: In this study, the fifth product of MODIS Terra called (Mod11C3 v005) with a spatial resolution of 5×5 kilometer and a Daytime  time period, which became monthly data after the necessary processing, was used. In this study, considering the significant precision of day-night-based physics algorithm, Wan et al. (2002) has used this method to study Daytime LST in Iran. Then, they were decoded and an array with the dimensions of 4855×62258 was obtained. Land surface temperature zoning was conducted by using the geostatistical method of kriging with the lowest error rate and the highest precision in mountainous areas.Results and discussion: The statistical characteristics of LST in Iran during different months showed that the highest average of LST in Iran with 46.1 ° C was in July. In the warm period of the year, and in particular, in the hot zones of Iran (the southern coasts) there is less variation in the temperature of the country, which consequently leads to less variation in LST in the country, and less spatial autocorrelation should be observed in the warm half of the year, which indicates a more stable temperature in the warm period of the year. The study of LST during the 15-year period from 2001 to 2015 based on the output of the MODIS sensor for different months of the year showed that the distribution of LST in Iran was severely affected by geographical conditions, especially its latitude and topographic condition.Conclusion: From the west to the east and from the north to the south, there was an increase in LST in all months of the year. The Lut desert is the warmest area in the country with the temperatures rising to 59° C in the warm days. The spatial processing of Daytime LST in Iran showed that LST was strongly affected by latitude and altitude, and the topographic conditions played an important role in the spatiotemporal distribution of LST, which is completely consistent with the studies conducted by who stated that each temperature range has a high degree of consistency with its environmental and geographical properties, in particular its elevation, latitude and slope characteristics.Although the temperature zones provided for the various months of the year have the considerable spatial continuity, the parts of a temperature cluster have appeared in the form of islands in other zones, indicating the effect of complex topographic and local conditions on the occurrence of these temperature islands compared to its surroundings, which causes a spatial variation in temperature and an increase in the desire to LST clustering in Iran, or in other words, to climatic implantation.

Keywords


  1. Ahmadi, M. and Dadashitoudbati, A., 2016. The biophysical effect of compound the formation of urban heat islands (Case study: Mashhad). Iranian Journal of Remote Sensing and GIS. 8 (3), 39-58. (In Persian with Englisg abstract).
  2. Alavipanah, S.K., 2011. The Principles of Remote Sensing and Interpretation of Satellite Imagery and Aerial Photographs. Tehran University Press, Tehran, Iran. (In Persian with English abstract).
  3. Aliabadi, K., Asadi Zangeneh, M. and Dadashi Roudbari, A., 2015. Evaluation and monitoring dust storm by using remote sensing (Case study: West and southwest of Iran). Journal of Rescue and Relief, 7(1), 1-20. (In Persian with Englisg abstract).
  4. Alijani, B., 2010. Weather Iran. Payam Noor University Press. Tehran, Iran. (In Persian with English abstract).
  5. Anderson, M.C., Norman, J.M., Kustas, W.P., Houborg, R., Starks, P.J. and Agam, N., 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment. 112(12), 4227-4241.
  6. Anisimov, O.A., Lobanov, V.A., Reneva, S.A., Shiklomanov, N.I., Zhang, T. and Nelson, F.E., 2007. Uncertainties in gridded air temperature fields and effects on predictive active layer modeling. Journal of Geophysical Research: Earth Surface. Vol. 112(F2), 1-12.
  7. Azizi, G., Alavipanah, S.K., Goodarzi, N. and Kazemi, M., 2007. An estimation of the temperature of Lut desert using MODIS sensor data. Biaban, 12, 7–15.
  8. Bunn, A.G., Goetz, S.J., Kimball, J.S. and Zhang, K., 2007. Northern high‐latitude ecosystems respond to climate change. Eos, Transactions American Geophysical Union. 88(34), 333-335.
  9. Chopping, M., Moisen, G.G., Su, L., Laliberte, A., Rango, A., Martonchik, J.V. and Peters, D.P., 2008. Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA multiangle imaging spectro-radiometer. Remote Sensing of Environment. 112(5), 2051-2063.
  10. Coll, C., Caselles, V., Galve, J.M., Valor, E., Niclos, R., Sanchez, J.M. and Rivas, R., 2005. Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote sensing of Environment. 97(3), 288-300.
  11. Duan, S.B., Li, Z. L., Tang, B.H., Wu, H. and Tang, R., 2014. Direct estimation of land-surface diurnal temperature cycle model parameters from MSG–SEVIRI brightness temperatures under clear sky conditions. Remote Sensing of Environment. 150, 34-43.
  12. Effat, H.A. and Hassan, O.A.K., 2014. Change detection of urban heat islands and some related parameters using multi-temporal Landsat images; A case study for Cairo city, Egypt. Urban Climate. 10, 171-188.
  13. Euskirchen, E.S., McGuire, A.D., Kicklighter, D.W., Zhuang, Q., Clein, J.S., Dargaville, R.J. and Romanovsky, V.E., 2006. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high‐latitude ecosystems. Global Change Biology. 12(4), 731-750.
  14. Fischer, M.M. and Getis, A., 2009. Handbook of applied spatial analysis: software tools, methods and applications. Springer Science and Business Media. p. 811.
  15. Hale, R.C., Gallo, K.P. and Loveland, T.R., 2008. Influences of specific land use/land cover conversions on climatological normals of near‐surface temperature. Journal of Geophysical Research: Atmospheres. 113(D14).
  16. Hartmann, D. L., Klein Tank, A. M., Rusticucci, M., Alexander, L. V., Brönnimann, S., Charabi, Y. A. R., and Soden, B. J., 2013. January. Observations. In Cambridge University Press.
  17. Hashemi, S., Alavipanah, S. and Dinarvandi, M., 2013. LST Assessment Using Thermal Remote Sensing in Urban Environment. Journal of Environmental Studies. 39(1), 81-92. (In Persian with Englisg abstract).
  18. Hashimoto, H., Dungan, J.L., White, M.A., Yang, F., Michaelis, A.R., Running, S.W. and Nemani, R.R., 2008. Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data. Remote Sensing of Environment. 112(1), 142-155.
  19. Holzman, M.E., Rivas, R. and Piccolo, M.C., 2014. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation. 28, 181-192.
  20. Kaviani, A., Sohrabi, A.T., Daneshkar Araste, P. 2013. Estimation of land surface temperature using NDVI in MODIS and Landsat ETM+ imageries. Journal of Agricultural Meteorology. 1(1), 14-25. (In Persian with Englisg abstract).
  21. Kim, Y., Kimball, J.S., Zhang, K. and McDonald, K.C., 2012. Satellite detection of increasing Northern Hemisphere non-frozen seasons from 1979 to 2008: Implications for regional vegetation growth. Remote Sensing of Environment. 121, 472-487.
  22. Li, Z.L., Tang, B.H., Wu, H., Ren, H., Yan, G., Wan, Z. and Sobrino, J.A., 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment. 131, 14-37.
  23. Lin, I.I., Chen, J.P., Wong, G.T., Huang, C.W. and Lien, C.C., 2007. Aerosol input to the South China Sea: Results from the MODerate resolution imaging spectro-radiometer, the quick scatterometer, and the measurements of pollution in the troposphere sensor. Deep Sea Research Part II: Topical Studies in Oceanography. 54(14), 1589-1601.
  24. Lin, X., Zhang, W., Huang, Y., Sun, W., Han, P., Yu, L. and Sun, F., 2016. Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features. Remote Sensing. 8(8), 629.
  25. Lu, J., Tang, R., Tang, H. and Li, Z.L., 2014. A new parameterization scheme for estimating surface energy fluxes with continuous surface temperature, air temperature, and surface net radiation measurements. Water Resources Research. 50(2), 1245-1259.
  26. Masoodian, B., 2010. Weather Iran. Sharia Toos Publishing of Mashhad. Mashhad. (In Persian with English abstract).
  27. McGuire, A.D., Chapin Iii, F.S., Wirth, C., Apps, M., Bhatti, J., Callaghan, T. and Onuchin, A., 2007. Responses of high latitude ecosystems to global change: Potential consequences for the climate system. In Terrestrial ecosystems in a changing World (pp. 297-310). Springer Berlin Heidelberg.
  28. Medeiros, S.D.S., Cecilio, R.A., de Melo Júnior, J.C. and da Silva Junior, J.L., 2005. Estimation and spatialization of minimum, mean and maximum air temperatures for the northeast region of Brazil. Revista Brasileira de Engenharia Agricola e Ambiental. 9(2), 247-255.
  29. Meyer, H., Katurji, M., Appelhans, T., Müller, M.U., Nauss, T., Roudier, P. and Zawar-Reza, P., 2016. Mapping Daily Air Temperature for Antarctica Based on MODIS LST. Remote Sensing. 8(9), 732.
  30. Mildrexler, D.J., Zhao, M. and Running, S.W., 2011. Satellite finds highest land skin temperatures on earth. Bulletin of the American Meteorological Society. 92(7), 855-860.
  31. Moradi, M., Salahi, B. and Masoodian, S.A., 2016. Land surface temperature zoning of Iran with MODIS data. Journal of natural Enviroment Hazards. 5 (7). 101-116. (In Persian).
  32. Nemani, R.R., Keeling, C.D., Hashimoto, H., Jolly, W.M., Piper, S.C., Tucker, C.J., and Running, S. W., 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science. 300(5625), 1560-1563.
  33. Ollinger, S.V. and Smith, M.L., 2005. Net primary production and canopy nitrogen in a temperate forest landscape: an analysis using imaging spectroscopy, modeling and field data. Ecosystems. 8(7), 760-778.
  34. Pampaloni, P. and Paloscia, S., 1985. Experimental relationships between microwave emission and vegetation features. International Journal of Remote Sensing. 6(2), 315-323.
  35. Rajasekar, U. and Weng, Q., 2009. Urban heat island monitoring and analysis using a non-parametric model: A case study of Indianapolis. ISPRS Journal of Photogrammetry and Remote Sensing. 64(1), 86-96.
  36. Raziei, T., 2017. Identification of precipitation regimes of Iran using multivariate methods. Journal of the Earth and Space Physics (JESP). 43(3), 673-695. (In Persian with Englisg abstract).
  37. Scherrer, D., Bader, M.K.F. and Körner, C., 2011. Drought-sensitivity ranking of deciduous tree species based on thermal imaging of forest canopies. Agricultural and forest meteorology. 151(12), 1632-1640.
  38. Schimel, D., Pavlick, R., Fisher, J.B., Asner, G.P., Saatchi, S., Townsend, P., and Cox, P., 2015. Observing terrestrial ecosystems and the carbon cycle from space. Global change biology. 21(5), 1762-1776.
  39. Schwarz, N., Schlink, U., Franck, U. and Großmann, K., 2012. Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—an application for the city of Leipzig (Germany). Ecological Indicators. 18, 693-704.
  40. Senanayake, I.P., Welivitiya, W.D.D.P. and Nadeeka, P.M., 2013. Remote sensing based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM+ data. Urban Climate. 5, 19-35.
  41. Seto, K.C., Sanchez-Rodriguez, R. and Fragkias, M., 2010. The new geography of contemporary urbanization and the environment. Annual review of environment and resources. 35, 167-194.
  42. Shakiba, A., Ziayan Firoozabadi, P., Ashoorloo, D. and Namdari, S., 2009. Analysis of the relationship between land use and land cover and urban heat islands in Tehran in Tehran, using ETM + data. Iranian Journal of Remote Sensing and GIS. 1 (1), 56-39. (In Persian with Englisg abstract).
  43. Simmons, A.J., Jones, P.D., da Costa Bechtold, V., Beljaars, A.C.M., Kållberg, P.W., Saarinen, S., and Wedi, N., 2004. Comparison of trends and low‐frequency variability in CRU, ERA‐40, and NCEP/NCAR analyses of surface air temperature. Journal of Geophysical Research: Atmospheres. 109(D24).
  44. Song, K., Wang, M., Du, J., Yuan, Y., Ma, J., Wang, M. and Mu, G., 2016. Spatiotemporal Variations of Lake Surface Temperature across the Tibetan Plateau Using MODIS LST Product. Remote Sensing. 8(10), 854.
  45. Stathopoulou, M. and Cartalis, C., 2009. Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. Remote Sensing of Environment. 113(12), 2592-2605.
  46. Stathopoulou, M., Synnefa, A., Cartalis, C., Santamouris, M., Karlessi, T. and Akbari, H., 2009. A surface heat island study of Athens using high-resolution satellite imagery and measurements of the optical and thermal properties of commonly used building and paving materials. International Journal of Sustainable Energy. 28(1-3), 59-76.
  47. Stone, B., Vargo, J. and Habeeb, D., 2012. Managing climate change in cities: will climate action plans work? Landscape and Urban Planning. 107(3), 263-271.
  48. Thome, K.J., Czapla-Myers, J.S. and Biggar, S.F., 2003, November. Vicarious calibration of Aqua and Terra MODIS. In Optical Science and Technology, SPIE's 48th Annual Meeting (pp.395-405). International Society for Optics and Photonics.
  49. Wan, Z., 2007. Collection-5 MODIS land surface temperature products users’ guide. ICESS. University of California, Santa Barbara.
  50. Wan, Z., 2008. New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote sensing of Environment. 112(1), 59-74.
  51. Wan, Z., Zhang, Y., Zhang, Q. and Li, Z.L., 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote sensing of Environment. 83(1-2), 163-180.
  52. Wan, Z., Zhang, Y., Zhang, Q. and Li, Z.L., 2004. Quality assessment and validation of the MODIS global land surface temperature. International journal of remote sensing. 25(1), 261-274.
  53. Wang, S., Zhang, M., Sun, M., Wang, B., Huang, X., Wang, Q. and Feng, F., 2015. Comparison of surface air temperature derived from NCEP/DOE R2, ERA-Interim, and observations in the arid northwestern China: a consideration of altitude errors. Theoretical and Applied Climatology. 119(1-2), 99-111.
  54. Weng, Q., 2009. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing. 64(4), 335-344.
  55. Zhang, Y., Chen, W., Smith, S.L., Riseborough, D.W. and Cihlar, J., 2005. Soil temperature in Canada during the twentieth century: Complex responses to atmospheric climate change. Journal of Geophysical Research: Atmospheres. 110(D3).
  56. Zhao, T., Guo, W. and Fu, C., 2008. Calibrating and evaluating reanalysis surface temperature error by topographic correction. Journal of Climate. 21(6), 1440-1446.