Volume 33, Issue 1 (2018)                   GeoRes 2018, 33(1): 174-190 | Back to browse issues page
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‎ 10.29252/geores.33.1.174

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Received: 2017/07/3 | Accepted: 2018/03/10 | Published: 2018/06/19
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Monitoring of the Night Time land Surface Temperature in Iran Based on Output of the MODIS
Authors Mahmoud Ahmadi *1, Hamzeh Ahmadi2
1- Department of Climatology ,Shahid Beheshti University, Tehran, Iran
2- Department of Agricultural Climatology,Hakim Sabzevari University, Sabzevar, Iran
Abstract   (4371 Views)
The estimation of land surface temperature (LST) is useful for a wide range of applications such as agriculture, urban heat island (UHI), Energy Management, climate related extreme and climate change studies. The MODIS LST products are created as a sequence of products beginning with a swath (scene) and progressing, through spatial and temporal transformations, to daily, eight-day and monthly global gridded products. The purpose of this study is to analyze the output data of MODIS in order to manage the night time temperature of both night and day in the geographical area of Iran. For this purpose, the MODIS data on land surface temperature which were generated using the day-night algorithm, were extracted for the 2001- 2015 period based on the output of the product called MOD11C3 (V5). Then a matrix with the dimensions of 62258 × 4855 was formed. 62,258 in the array of cells with a spatial resolution of 5 km and 4855 representative designated days, which in the next step were reduced to 62 258 × 12 arrays. 12 being the representative of the months of the year. The final analysis of surface temperatures was evaluated using kriging. The results show that the coldest overnight temperatures of land surface happen in December through February, reaching to -20 degrees Celsius. Also regarding geographical locations, the pattern matches the ripples, especially the western half, North and North-West and North-Khorasan. September is the transition period from warm to cold nocturnal land surface temperature, and March is the transition period from cold to warm temperature. A focus of warm temperature of land surface could be observed in different months of the year. These warm temperatures are dominant in warm areas from April through September. The variety of nocturnal land surface temperature in Iran is high. Heights and ripples are significant factors in determining the nocturnal land surface temperature in different areas of Iran.
© 2018 Geographical Researches
Keywords:
Night Land Surface Temperature, LSTNT, Modis Terra, Satellite Images, Iran.
References
1. Adab, H., Amirahmadi, A., Atabati, A. (2014), Relating Vegetation Cover with Land Surface Temperature and Surface Albedo in Warm Period of Year Using MODIS Imagery in North of Iran, Physical Geography Research Quarterly, Vol. 46, No. 4, pp. 419-434. (In Persian).
2. Ahmadi, M., Dadashi Roudbari, A.A. (2016), The Effects of Biophysical Compounds on Urban Heat Islands Formation (Case Study: Mashhad), Iranian Remote Sensing GIS, Vol. 8, No. 3, pp. 39-58. (In Persian).
3. Aliabadi, K., Asadi, Z.M. Dadashi Roudbari, A.A. (2015), Evaluation and Monitoring Dust Storm by Using Remote Sensing (Case Study: West and Southwest of Iran), Journal of Rescue Relief, Vol. 7, No. 1, pp. 1-20. (In Persian).
4. Aliabadi, K., Dadashiroudbari, A.A. (2017), The Role of Geographic Components on the Temperature Dispersion at Urban Area Using Remote Sensing Techniques Case Study of Mashhad City, Geographical Planning of Space, Vol. 7, No. 24, pp. 131-142. (In Persian).
5. Alijani, B. (2010), Climate Iran. Payam Noor University Press. Tehran, Iran (In Persian).
6. Bechtel, B. (2015), A New Global Climatology of Annual Land Surface Temperature, Remote Sensing, Vol. 7, No. 3, pp. 2850-2870.
7. Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., Santos, A. (2012), Estimating Air Surface Temperature in Portugal Using MODIS LST Data, Remote Sensing of Environment, Vol. 124, pp. 108-121.
8. Carlson, T. (2007), An Overview of the Triangle Method for Estimating Surface Evapotranspiration and Soil Moisture from Fatellite Imagery, Sensors, Vol. 7, No. 8, pp. 1612-1629.
9. Chopping, M., Moisen, G.G., Su, L., Laliberte, A., Rango, A., Martonchik, J. V., Peters, D. P. (2008), Large Area Mapping of Southwestern Forest Crown Cover, Canopy Ceight, and Biomass Using the NASA Multiangle Imaging Spectro-Radiometer, Remote Sensing of Environment, Vol. 112, No,5, pp. 2051-2063.
10. DeVISSER, M.A. R. K., Messina, J. P., Moore, N.J., Lusch, D.P., Maitima, J. (2010), A Dynamic Species Distribution Model of Glossina Subgenus Morsitans the Identification of Tsetse Reservoirs and Refugia, Ecosphere, Vol. 1, No. 1, pp. 1-21.
11. Duan, S. B., Li, Z. L., Tang, B. H., Wu, H., Tang, R. (2014), Generation of a Time-Consistent Land Surface Temperature Product from MODIS Data, Remote Sensing of Environment, Vol. 140, pp. 339-349.
12. EPA (2017), Reducing Urban Heat Islands: Compendium of Strategies. Chapter 1: Urban Heat Island Basics, U. S, Environmental Protection Agency.
13. Hulley, G. C., Hook, S. J. (2009), Intercomparison of Versions 4, 4.1 and 5 of the MODIS Land Surface Temperature and Emissivity Products and Validation with Laboratory Measurements of Sand Samples from the Namib Desert, Namibia. Remote Sensing of Environment, Vol. 113, No. 6, pp. 1313-1318.
14. IPCC (2007), Climate Change 2007 the Physical Science Basis in Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, United Kindom, Cambridge.
15. Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., Goldberg, A. (2010), Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations, Journal of climate, Vol. 23, No. 3, pp. 618-633.
16. Keramitsoglou, I., Kiranoudis, C. T., Ceriola, G., Weng, Q., Rajasekar, U. (2011), Identification and Analysis of Urban Surface Temperature Patterns in Greater Athens, Greece, Using MODIS Imagery, Remote Sensing of Environment, Vol. 115, No. 12, pp. 3080-3090.
17. Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., Sobrino, J. A. (2013), Satellite-Derived Land Surface Temperature: Current Status and Perspectives, Remote Sensing of Environment, Vol. 131, pp. 14-37.
18. Li, Z.L., Tang, R., Wan, Z., Bi, Y., Zhou, C., Tang, B., Zhang, X. (2009), A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data, Sensors, Vol. 9, No. 5, pp. 3801-3853.
19. Ma, X. L., Wan, Z., Moeller, C.C., Menzel, W. P., Gumley, L. E. (2002), Simultaneous Retrieval of Atmospheric Profiles, Land-Surface Temperature, and Surface Emissivity from Moderate-Resolution Imaging Spectroradiometer Thermal Infrared Data: Extension of a Two-Step Physical Algorithm, Applied optics, Vol. 41, No. 5, pp. 909-924.
20. Ma, X.L., Wan, Z., Moeller, C.C., Menzel, W.P., Gumley, L.E., Zhang, Y. (2000), Retrieval of Geophysical Parameters from Moderate Resolution Imaging Spectroradiometer Thermal Infrared Data: Evaluation of a Two-Step Physical Algorithm, Applied Optics, Vol. 39, No. 20, pp. 3537-3550.
21. Masoudian, S.A. (2011), Climate Iran, Mashhad, First Printing, Mashhad Sharia Toos Pub. (In Persian).
22. Mohammadi, H. (2011), Urban Climatology, Tehran, Tehran University Pub. (In Persian).
23. Moradi, M., Salahi, B., Masoodian, S. (2016), Land Surface Temperature Zoning of Iran with MODIS Data, Natural Environmental Hazards, Vol. 5, No. 7, pp. 101-116. (In Persian).
24. Moradi, M., Salahi, B., Masoodian, S. (2016). Analysis of Land Surface Temperature Gradient of Iran Using MODIS Terra and Aqua Data, Physical Geography Research Quarterly, Vol. 48, No. 4, pp. 517-532. (In Persian).
25. Moran, M. S. (2004), Thermal Infrared Measurement as an Indicator of Plant Ecosystem Health, Thermal remote sensing in land surface processes, pp. 257-282.
26. Rahimi khoob, A., Kouchakzade, M., Mohammadvali Samani, J., Sharifi, F. (2005), Estimating Maximum Daily Temperature Using NOAA Satellite Images: Case Study in Oroomieh Lake Basin, Pajouhesh & Sazandegi, No. 68, pp. 84-90. (In Persian).
27. Sun, H., Chen, Y., Gong, A., Zhao, X., Zhan, W., Wang, M. (2014), Estimating Mean Air Temperature Using MODIS Day and Night Land Surface Temperatures, Theoretical and applied climatology, Vol. 118, No. 1-2, pp. 81-92.
28. Tomlinson, C. J., Chapman, L., Thornes, J. E., Baker, C. (2011), Remote Sensing Land Surface Temperature for Meteorology and Climatology a Review, Meteorological Applications, Vol. 18, No. 3, pp. 296-306.
29. Tomlinson, C. J., Chapman, L., Thornes, J. E., Baker, C. J., Prieto-Lopez, T. (2012), Comparing Night-Time Satellite Land Surface Temperature from MODIS and Ground Measured Air Temperature Across a Conurbation, Remote sensing letters, Vol. 3, No. 8, pp. 657-666.
30. Tran, H., Uchihama, D., Ochi, S., Yasuoka, Y. (2006), Assessment with Satellite Data of the Urban Heat Island Effects in Asian Mega Cities, International Journal of Applied Earth Observation and Geoinformation, Vol. 8, No. 1, pp. 34-48.
31. Trigo, I. F., Monteiro, I. T., Olesen, F., Kabsch, E. (2008), An Assessment of Remotely Sensed Land Surface Temperature, Journal of Geophysical Research Atmospheres, Vol. 113, No. D17, pp. 1-12.
32. Wan, Z., Dozier, J. (1996), A Generalized Split-Window Algorithm for Retrieving Land-Surface Temperature from Space, IEEE Transactions on geoscience and remote sensing, Vol. 34, No. 4, pp. 892-905.
33. Wan, Z., 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, Vol. 35, No. 4, pp. 980-996.
34. Wan, Z., Snyder, W. (2012), MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD), Vol. 3.
35. Wan, Z., Zhang, Y., Zhang, Q., Li, Z. L. (2002), Validation of the Land-Surface Temperature Products Retrieved from Terra Moderate Resolution Imaging Spectroradiometer Data, Remote sensing of Environment, Vol. 83, No. 1, pp. 163-180.
36. Wan, Z., Zhang, Y., Zhang, Q., Li, Z. L. (2004), Quality Assessment and Validation of The MODIS Global Land Surface Temperature, International Journal of Remote Sensing, Vol. 25, No. 1, pp. 261-274.
37. Wang, K., Dickinson, R.E. (2012), A Review of Global Terrestrial Evapotranspiration Observation, Modeling, Climatology, and Climatic Variability, Reviews of Geophysics, Vol. 50, No. 2, pp. 1-54.
38. Wang, W., Liang, S., Meyers, T. (2008), Validating MODIS Land Surface Temperature Products Using Long-Term Nighttime Ground Measurements, Remote Sensing of Environment, Vol. 112, No. 3, pp. 623-635.
39. Weng, Q., Fu, P., GAO, F. (2014), Generating Daily Land Surface Temperature at Landsat Resolution by Fusing Landsat and MODIS Data, Remote Sensing of Environment, Vol. 145, 55-67.
40. Willmott, C. J., Robeson, S. M. (1995), Climatologically Aided Interpolation (CAI) of Terrestrial Air Temperature, International Journal of Climatology, Vol. 15, No, 2, pp. 221-229.
41. Yang, J., Wang, Y. (2011), Estimating Evapotranspiration Fraction by Modeling Two-Dimensional Space of NDVI/Albedo and Day–Night Land Surface Temperature Difference a Comparative Study, Advances in Water Resources, Vol. 34, No. 4, pp. 512-518.
42. Zhu, W., Lű, A., Jia, S. (2013), Estimation of Daily Maximum and Minimum Air Temperature Using MODIS Land Surface Temperature Products, Remote Sensing of Environment, Vol. 130, pp. 62-73.