Enhanced Adaptive Technique for Surface Temperature Variability Analysis

  • Deepak Kumar
  • Tavishi Tewary
  • Sulochana Shekhar
Review Paper


The concerns of global warming effect have elevated the global inclination to interpret the mystery of surface temperature variations with respect to various aspects. Surface temperature (ST) plays a role as most substantial parameter in any environment. The effort attempts to present the satellite image processing methods for utilizing the state-of-the-art-enhanced adaptive technique (AET) to illustrate the spatial variability of ST. These methods can be helpful in computing the spatial variability at macro- to micro-scales. Therefore, spatial variability through AET was explored to demonstrate spatial scattering of surface temperatures. The outcomes seemingly revealed the aggregation and dispersion of spatial thermal configuration at the test area. The current work also presented the approach for assimilation of spatial variability information as a powerful reliable instrument to monitor the thermal dynamics within the region.


Adaptive Enhanced Global Spatial Surface Temperature 


Author contributions

Dr. DK conceived and designed the study, Ms. Tavishi performed the research, analyzed the data, and Dr. SS assessed the manuscript. Ms. Tavishi assisted with writing, and contributed editorial input.

Compliance with Ethical Standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.


  1. Alipour T, Esmaeily A (2003) Land surface temperature estimation from thermal band of landsat sensor, case study: Alashtar city. In: The international archives of the photogrammetry, remote sensing and spatial information sciences, Vol. XXXVIII-4/C7Google Scholar
  2. Barsi JA, Hook SJ, Palluconi FD, Schott JR, Raqueno NG (2006) Landsat TM and ETM+ thermal band calibration space science and applications, Inc, NASA/GSFC, Code 614. 4, Greenbelt, MD 20781; NASA/JPL Pasadena, CA 91109; 6296-16V. 4 (p 1 of 9)/Color:No/Format:Letter/Date:7/18/2006 12, 4(1), pp 1–9Google Scholar
  3. Dontree S (2010) Relation of land surface temperature (LST) and land use/land cover (LULC) from remotely sensed data in Chiang Mai—Lamphun Basin. In: SEAGA 2010, Hanoi, pp 1–11Google Scholar
  4. Irish R, Gsfc S (2008) Calibrated landsat digital number (DN) to top of atmosphere (TOA) reflectance conversionGoogle Scholar
  5. Jones HG, Schofield P (2008) Thermal and another remote sensing of plant stress. Gen Appl Plant Physiol 34:19–32.
  6. Kumar D, Shekhar S (2015) Statistical analysis of land surface temperature—vegetation indexes relationship through thermal remote sensing. Ecotoxicol Environ Saf 2:1–6. Scholar
  7. Li F (2004) Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX. Remote Sens Environ 92(4):521–534. Scholar
  8. Moonen P, Defraeye T, Dorer V, Blocken B, Carmeliet J (2012) Urban Physics: effect of the micro-climate on comfort, health, and energy demand. Front Archit Res 1(3):197–228. Scholar
  9. Norman JM, Becker F (1995) Terminology in thermal infrared remote sensing of natural surfaces. Agric For Meteorol 77(3–4):153–166. Scholar
  10. Shao J, Swanson JC, Patterson R, Lister PJ, McDonald AN (1997) Variation of winter road surface temperature due to topography and application of thermal mapping. Meteorol Appl 4(2):131–137. Scholar
  11. Urban F, Benders RMJ, Moll HC (2007) Corrigendum to “Modelling energy systems for developing countries”. [Energy Policy 35 (2007) 3473–3482] ( Energy Policy, 35, 4765.
  12. Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384. Scholar

Copyright information

© Shiraz University 2018

Authors and Affiliations

  • Deepak Kumar
    • 1
  • Tavishi Tewary
    • 2
  • Sulochana Shekhar
    • 3
  1. 1.Amity Institute of Geoinformatics and Remote Sensing (AIGIRS)Amity UniversityNoidaIndia
  2. 2.Amity Business School (ABS)Amity UniversityNoidaIndia
  3. 3.School of Earth SciencesCentral University of Tamil NaduThiruvarurIndia

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