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 language | English |
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Pages (from-to) | 655-669 |
Number of pages | 15 |
Journal | European Journal of Remote Sensing |
Volume | 47 |
Issue number | 1 |
DOIs | |
Publication status | Published - 12 Sept 2014 |
Bibliographical note
Publisher Copyright:© 2014, Associazione Italiana di Telerilevamento. All rights reserved.
Keywords
- Climate
- Land surface temperature
- Landsat
- Remote sensing