Document Type : Research Paper
Authors
1
Ph.D. Student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
2
Professor of Climatology, Department of Physical Geography, University of Mohaghegh Ardabili, Ardabil, Iran.
3
Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Iran.
4
Professor , Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
10.22034/gp.2026.69514.3462
Abstract
In the present study, Land Surface Temperature (LST) for Ardabil County was retrieved using OLI sensor images from the Landsat 8 satellite, applying the Single-Channel (SC) and SEBAL algorithms. In the next step, the results were validated using SLSTR sensor data from the Sentinel-3 satellite and finally compared with synoptic station data of the study area. Overall, the Single-Channel algorithm estimated higher surface temperatures compared to the SEBAL algorithm. This difference can be attributed to the fact that SEBAL utilizes both thermal bands 10 and 11 for LST retrieval, while the computational parameters in the two algorithms differ. Despite its more complex computational process, the SEBAL algorithm produced more realistic and accurate LST estimations. Furthermore, comparison between thermal bands 10 and 11 within the SEBAL framework indicated that band 10, due to its higher signal quality, is more suitable for LST extraction, although combining both bands can further improve accuracy. Daily temperature fluctuations were observed in both algorithms; however, their magnitude was more pronounced in the Single-Channel method. Moreover, by analyzing the difference between the daily mean temperature derived from Landsat 8 satellite data and the temperature recorded at the synoptic station, the SEBAL algorithm demonstrated better agreement with actual ground temperature, with a lower mean temperature difference (16.21 °C) compared to the Single-Channel method (25.60 °C) during the statistical period. Results from linear regression analysis and correlation coefficient calculations revealed an average correlation (R) of 0.96, confirming the high accuracy and reliability of the estimated values. This study highlights the importance of careful selection of retrieval algorithms and satellite sensors, as well as the influence of environmental factors on temperature variability, emphasizing that data validation is a crucial step in both scientific and applied research.
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