Land surface temperature retrieval for climate analysis and association with climate data

Emre Ozelkan*, Serdar Bagis, Ertunga Cem Ozelkan, Burak Berk Ustundag, Cankut Ormeci

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

The aim of this study is to demonstrate the relationship between the long years’ monthly average (LYMA) land surface temperature (LST) and the LYMA air temperature (Ta), the total precipitation (Pt), and the relative humidity (RH). Data from 27 meteorological stations in the Eastern Thrace region and corresponding thermal infrared images from Landsat-5 (TM) and Landsat-7 (ETM+) were used in this study. Simple regression models were developed for each meteorological station to predict the LYMA Ta, Pt and RH based on the LST values. The resulting LST-based prediction models were judged based on the correlation coefficient (r) and root mean square (RMSE). The average correlation and RMSE for the LST-based Ta were r = 0.959 and RMSE = 1.771 °C. The average correlation and RMSE for the LST-based Pt were r =-0.863 and RMSE = 10.098 mm. The average correlation and RMSE for the LST-based RH were r =-0.932 and RMSE = 1.875%. The results indicate that LST can be a good estimator for LYMA Ta, Pt and RH, and LYMA Ta is positively, LYMA Pt and LYMA RH are negatively correlated with LYMA LST.

Original languageEnglish
Pages (from-to)655-669
Number of pages15
JournalEuropean Journal of Remote Sensing
Volume47
Issue number1
DOIs
Publication statusPublished - 12 Sept 2014

Bibliographical note

Publisher Copyright:
© 2014, Associazione Italiana di Telerilevamento. All rights reserved.

Keywords

  • Climate
  • Land surface temperature
  • Landsat
  • Remote sensing

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