Retrieval of monthly maximum and minimum air temperature using MODIS aqua land surface temperature data over the United Arab Emirates

Abduldaem S. Alqasemi*, Mohamed E. Hereher, Ayad M.Fadhil Al-Quraishi, Hakim Saibi, Ala Aldahan, Abdelgadir Abuelgasim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Spatially distributed air temperature (Ta) data are essential for environmental studies. Ta data are collected from meteorological stations of sparse distribution. This problem can be overcome by using remotely sensed datasets at different scales. This study used land-based temperature measurements and satellite data for estimating Ta distribution over the United Arab Emirates. Land-based Ta data from 11 weather stations during 2003 to 2019 were used with MODIS Aqua LST for both daytime (LSTd) and nighttime (LSTn) data. The results indicate a significant correlation between LST and Ta with regression coefficients R2 > 0.94/0.96 and Root Mean Square Error about 1.75/0.97 °C of LSTd/Tmax and LSTn/Tmin, respectively. Large variability was observed between the daytime and nighttime mean temperature distribution indicating the importance of MODIS LST as a proxy for Ta. These countrywide Ta grids provide vital tools for the planning of environmental and economic developments in the era of global climate change.

Original languageEnglish
JournalGeocarto International
Publication statusAccepted/In press - 2020
Externally publishedYes


  • Air temperature
  • land surface temperature
  • linear regression
  • meteorological station data
  • UAE

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Water Science and Technology

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