Investigation of seasonal distribution and abnormal trend of day and night surface temperature in Iran using MODIS sensor data

Document Type : Full length article


1 Associate Professor of Climatology in Shahid Beheshti University, Tehran, Iran

2 Masters student of Environmental meteorology, Shahid Beheshti University, Faculty of Earth Sciences, Tehran.

3 Postdoctoral Researcher of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad.



Land surface temperature (LST) plays an important role in surface energy balance. A set of environmental parameters, such as temporal and geographical changes, thermal properties, biophysical properties, climatic parameters and subsurface conditions can cause heterogeneous spatio-temporal distribution of LST and its anomalies to be. LULCC-induced surface temperature anomalies have important implications for understanding the physical mechanisms associated with the surface to changes in various biophysical factors, including albido and surface roughness (also known as aerodynamic resistance). The purpose of this study is to evaluate the seasonal changes and abnormalities of daytime and nighttime land surface temperature in Iran based on LST derived from satellite data.

Materials and methods,

In this study, the following steps were performed:

A study area:

The whole country of Iran was wanted. To better reveal the behavior of surface temperature anomalies in Iran, the data has been converted to a seasonal scale and also for the first time in the country, surface temperature anomalies have been studied separately for night and day.

B) Data

B-1) Moderate Resolution Imaging Spectroradiometer(MODIS)

To investigate the anomaly of land surface temperature, the MODIS sensor data of Terra satellite MOD_LSTAD and MOD_LSTAN products were used for day and night data with a horizontal separation of 10 km and the statistical period of 2001-2018, respectively.

C) Calculate trend and trend slope using non-parametric Mann-Kendall and Sen’s tests

In order to evaluate the abnormal trend of land surface temperature in Iran, non-parametric Mann-Kendall (M-K) test was used. The non-parametric Sen's method was used to estimate the slope of the process in the time series of land surface temperature anomalies and day and night in Iran.

Results and discussion,

The results showed that the mean anomaly of daytime land surface temperature in Iran (LSTAD) in the three seasons of winter, spring and autumn is negative and in summer is positive. Also, the long-term mean anomaly of night surface temperature (LSTAN) is negative in cold seasons (winter and autumn) and positive in warm seasons. The positive maximum of LSTAD in Iran was 0.172 in summer and its negative maximum was -0.672 in autumn. The same statistical quantity was obtained for LSTAN positive anomaly in summer 0.266 and in autumn 0.244. The minimum LSTAD was calculated between -1.942 to -3.097 and the maximum was calculated between 1.047 to 2.865. For night, it showed a minimum between -0.748 to -1.296 and a maximum between 1.597 to 2.189. The average statistical trend of Iran LSTAD and LSTAN in all seasons except autumn is increasing. This amount, despite being incremental, is not significant. During the day, the maximum average trend of increasing abnormality is obtained in summer (0.744) and at night in spring (1.038). The minimum and maximum trends in both day and night in Iran are significant at the alpha level of 0.01 and in terms of trend intensity, the warm seasons are more intense. The highest computational Z-score of Mann-Kendall test was obtained at night with the value of 4.097 (spring). Also, the same maximum amount per day was calculated with the amount of 3.917 in summer.


In this study, we have evaluated the day and night land surface temperature anomaly of Iran using Terra satellite MODIS sensor data during a long-term statistical period (2001-2018). The non-parametric Mann-Kendall test was used to study the trend and the non-parametric Sen test was used to calculate the trend slope. The positive anomaly of Iran's land surface temperature is higher at night than during the day and this amount is also significant in the warm seasons of the year. The maximum positive anomaly was obtained during the day during the summer with a value of 0.172 degrees Celsius and for the night with a value of 0.266 degrees Celsius. The average anomaly trend of land surface temperature during the day and night in winter to summer is increasing and only in autumn this amount is decreasing. The minimum and maximum trend in each period of time is significant at the alpha level of 0.01 and the intensity of the trend is more at night than during the day. The main focus of negative anomalies is recognizable in low-lying dry areas, inland arid regions located in the east and southeast of Iran and inland holes of Iran. While the increasing anomaly in the highlands and high latitudes of Iran is significant. Also, the dominant upward trend can be seen in the highlands of Iran, except in autumn; In this regard (Fallah Ghalhari, Shakeri and Dadashi Roudbari,2019) who used three methods of microcirculation SDSM, MarkSimGCM and CORDEX simulated the minimum and maximum temperature of Iran under the models CanESM2, GFDL-ESM2M and MPI-ESM-LR up to 2100 ; It was concluded that the annual temperature anomalies of the selected models are at high latitudes and mountainous highlands, which is in line with the results obtained in this study. One of the most important roles of land surface temperature and its anomaly is changes in convective processes, mixture layer depth and wind speed. Therefore, increasing the anomaly of land surface temperature in Iran can increase convection on the one hand and change the regional wind speed. (Dadashi Roudbari,1399) in explaining the role of surface temperature and climate change has stated that the warm surface of convection increases and causes the mixing of surface air and high surface air. Since the velocities of horizontal winds at land level are zero and at higher levels, the vertical mixing of horizontal winds causes wind speeds close to the earth's land surface to increase and wind speeds at high levels to decrease. Variability in surface temperature also changes the air temperature near the surface. In addition to what has been said, land surface warming in the highlands of Alborz and Zagros also affects the carbon cycle; Because surface heating accelerates the melting of snow and ice in these areas, resulting in the release of excess carbon (Fili, Roir, Gotha, & Pregent, 2003). Therefore, it is worthwhile to pay more attention to policies related to carbon stabilization as well as programs related to water resources and dam construction based on what was addressed in this study.


Main Subjects

احمدی، محمود؛ داداشی رودباری، عباسعلی و احمدی، حمزه (1397 الف). پایش دمای شب‏هنگام سطح زمین در گسترة ایران مبتنی بر برون‏داد سنجندة MODIS، فصل‏نامة تحقیقات جغرافیایی، ۳۳ (۱): ۱۷۴-۱۹۰.
احمدی، محمود؛ داداشی رودباری، عباسعلی و احمدی، حمزه (1397 ب). واکاوی دمای روزهنگام سطح زمین ایران مبتنی بر برون‏داد سنجندة MODIS، فصل‏نامة علوم محیطی، 16(1): 47-68.
داداشی رودباری، عباسعلی (1399). واکاوی وردایی زمانی- مکانی الگوهای قائم و افقی ریزگردها و ارزیابی بازخوردهای آب‏وهوایی آن در ایران، رسالة دکتری آب‏وهواشناسی، دانشکدة علوم زمین، دانشگاه شهید بهشتی.
مرادی، مسعود؛ صلاحی، برومند و مسعودیان، سیدابوالفضل (1395). پهنه‏بندی دمای رویة زمین ایران با داده‏های مودیس، مجلة مخاطرات محیط طبیعی، ۵(7):  ۱۰۱-116.
مسعودیان، سیدابوالفضل (1390). آب‏وهوای ایران، مشهد: شریعة توس.
Alkama, R. and Cescatti, A. (2016). Biophysical climate impacts of recent changes in global forest cover. Science, 351(6273): 600-604.
Bellaoui, M.; Hassini, A. and Bouchouicha, K. (2017). Remote sensed land surface temperature anomalies for earthquake prediction. In International Journal of Engineering Research in Africa (Vol. 31, pp. 120-134). Trans Tech Publications Ltd.
Benz, S. A.; Davis, S. J. and Burney, J. A. (2021). Drivers and projections of global surface temperature anomalies at the local scale. Environmental Research Letters.
Berger, C.; Rosentreter, J.; Voltersen, M.; Baumgart, C.; Schmullius, C. and Hese, S. (2017). Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature. Remote sensing of environment, 193: 225-243.
Bhardwaj, A.; Singh, S.; Sam, L.; Joshi, P. K.; Bhardwaj, A.; Martín-Torres, F. J. and Kumar, R. (2017). A review on remotely sensed land surface temperature anomaly as an earthquake precursor. International journal of applied earth observation and geoinformation, 63: 158-166.
Boisier, J. P.; de Noblet‐Ducoudré, N.; Pitman, A. J.; Cruz, F. T.; Delire, C.; Van den Hurk, B. J. J. M.; ... and Voldoire, A. (2012). Attributing the impacts of land‐cover changes in temperate regions on surface temperature and heat fluxes to specific causes: Results from the first LUCID set of simulations. Journal of Geophysical Research: Atmospheres, 117(D12).
Coolbaugh, M. F.; Kratt, C.; Fallacaro, A.; Calvin, W. M. and Taranik, J. V. (2007). Detection of geothermal anomalies using advanced spaceborne thermal emission and reflection radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA. Remote Sensing of Environment, 106(3): 350-359.
Dadashi-Roudbari, A. and Ahmadi, M. (2020). Evaluating temporal and spatial variability and trend of aerosol optical depth (550 nm) over Iran using data from MODIS on board the Terra and Aqua satellites. Arabian Journal of Geosciences, 13(6): 1-23.
Duhan, D. and Pandey, A. (2013). Statistical analysis of long term spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India. Atmospheric Research, 122: 136-149.
Fallah-Ghalhari, G.; Shakeri, F. and Dadashi-Roudbari, A. (2019). Impacts of climate changes on the maximum and minimum temperature in Iran. Theoretical and Applied Climatology, 138(3-4): 1539-1562.
Fily, M.; Royer, A.; Goıta, K. and Prigent, C. (2003). A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas. Remote Sensing of Environment, 85(3): 328-338.
Giorgi, F.; Hurrell, J. W.; Marinucci, M. R. and Beniston, M. (1997). Elevation dependency of the surface climate change signal: a model study. Journal of Climate, 10(2): 288-296.
Harris, P. P.; Folwell, S. S.; Gallego-Elvira, B.; Rodríguez, J.; Milton, S. and Taylor, C. M. (2017). An evaluation of modeled evaporation regimes in Europe using observed dry spell land surface temperature. Journal of Hydrometeorology, 18(5): 1453-1470.
Houghton, J. T.; Ding, Y. D. J. G.; Griggs, D. J.; Noguer, M.; Van der Linden, P. J.; Dai, X.; ... and Johnson, C. A. (2001). Climate change 2001: the scientific basis. The Press Syndicate of the University of Cambridge.
Jia, L.; Marco, M.; Bob, S.; Lu, J. and Massimo, M. (2017). Monitoring water resources and water use from earth observation in the belt and road countries. Bulletin of Chinese Academy of Sciences, 32(Z1): 62-73.
Jin, M. and Dickinson, R. E. (2010). Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters, 5(4): 044004.
Kendall, M. G. (1955). Rank correlation methods.
King, M. D. (1999). EOS science plan: the state of science in the EOS program. National Aeronautics and Space Administration.
Li, Y.; Zhao, M.; Motesharrei, S.; Mu, Q.; Kalnay, E. and Li, S. (2015). Local cooling and warming effects of forests based on satellite observations, Nat. Commun, 6: 6603.
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245-259.
Mattar, C.; Franch, B.; Sobrino, J. A.; Corbari, C.; Jiménez-Muñoz, J. C.; Olivera-Guerra, L.; ... and  Mancini, M. (2014). Impacts of the broadband albedo on actual evapotranspiration estimated by S-SEBI model over an agricultural area. Remote sensing of environment, 147: 23-42.
Mildrexler, D. J.; Zhao, M. and Running, S. W. (2011). A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. Journal of Geophysical Research: Biogeosciences, 116(G3).
Naudts, K.; Chen, Y.; McGrath, M. J.; Ryder, J.; Valade, A.; Otto, J. and Luyssaert, S. (2016). Europe’s forest management did not mitigate climate warming. Science, 351(6273): 597-600.
Oku, Y.; Ishikawa, H.; Haginoya, S. and Ma, Y. (2006). Recent trends in land surface temperature on the Tibetan Plateau. Journal of climate, 19(12): 2995-3003.
Panah, S. K.; Mogaddam, M. K. and Firozjaei, M. K. (2017). Monitoring Spatiotemporal Changes of Heat Island in Babol City Due to Land Use Changes. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42.
Qin, J.; Yang, K.; Liang, S. and Guo, X. (2009). The altitudinal dependence of recent rapid warming over the Tibetan Plateau. Climatic Change, 97(1-2): 321.
Rigden, A. J. and Li, D. (2017). Attribution of surface temperature anomalies induced by land use and land cover changes. Geophysical Research Letters, 44(13): 6814-6822.
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324): 1379-1389.
Solangi, G. S.; Siyal, A. A. and Siyal, P. (2019). Spatiotemporal dynamics of land surface temperature and its impact on the vegetation. Civil Engineering Journal, 5(8): 1753-1763.
Stocker, T. F.; Qin, D.; Plattner, G. K.; Tignor, M.; Allen, S. K.; Boschung, J.; ... and Midgley, P. M. (2013). Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, 1535.
Velde, R.; Su, Z.; Ek, M.; Rodell, M. and Ma, Y. (2009). Influence of thermodynamic soil and vegetation parameterizations on the simulation of soil temperature states and surface fluxes by the Noah LSM over a Tibetan plateau site. Hydrology and Earth System Sciences, 13(6): 759-777.
Weng, Q.; Firozjaei, M. K.; Kiavarz, M.; Alavipanah, S. K. and Hamzeh, S. (2019). Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Science of the Total Environment, 650: 515-529.
Weng, Q.; Hu, X.; Quattrochi, D. A. and Liu, H. (2013). Assessing intra-urban surface energy fluxes using remotely sensed ASTER imagery and routine meteorological data: A case study in Indianapolis, USA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10): 4046-4057.
Xiong, X.; Chiang, K.; Sun, J.; Barnes, W. L.; Guenther, B. and  Salomonson, V. V. (2009). NASA EOS Terra and Aqua MODIS on-orbit performance. Advances in Space Research, 43(3): 413-422.
Xue, Y.; Diallo, I.; Li, W.; David Neelin, J.; Chu, P. C.; Vasic, R.; ... and Fu, C. (2018). Spring land surface and subsurface temperature anomalies and subsequent downstream late spring‐summer droughts/floods in North America and East Asia. Journal of Geophysical Research: Atmospheres, 123(10): 5001-5019.
Yan, Y.; Mao, K.; Shi, J.; Piao, S.; Shen, X.; Dozier, J.; ... and Bao, Q. (2020). Driving forces of land surface temperature anomalous changes in North America in 2002–2018. Scientific reports, 10(1): 1-13.
Yang, J.; Ren, J.; Sun, D.; Xiao, X.; Xia, J. C.; Jin, C. and Li, X. (2021). Understanding land surface temperature impact factors based on local climate zones. Sustainable Cities and Society, 69: 102818.
Zhang, X.; Estoque, R. C. and Murayama, Y. (2017). An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustainable cities and society, 32: 557-568.
Zhao, L.; Ping, C. L.; Yang, D.; Cheng, G.; Ding, Y. and Liu, S. (2004). Changes of climate and seasonally frozen ground over the past 30 years in Qinghai–Xizang (Tibetan) Plateau, China. Global and Planetary Change, 43(1-2): 19-31.
Salama, M. S., Van der Velde, R., Zhong, L., Ma, Y., Ofwono, M., & 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(3), 769-781.
Volume 53, Issue 3
December 2021
Pages 351-364
  • Receive Date: 07 May 2021
  • Revise Date: 01 August 2021
  • Accept Date: 13 September 2021
  • First Publish Date: 21 September 2021