Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada)

David Hidalgo García,Julián Arco Díaz

doi:10.3390/rs13051012

Abstract

Over the past decade, satellite imaging has become a habitual way to determine the land surface temperature (LST). One means entails the use of Landsat 8 images, for which mono window (MW), single channel (SC) and split window (SW) algorithms are needed. Knowing the precision and seasonal variability of the LST can improve urban climate alteration studies, which ultimately help make sustainable decisions in terms of the greater resilience of cities. In this study we determine the LST of a mid-sized city, Granada (Spain), applying six Landsat 8 algorithms that are validated using ambient temperatures. In addition to having a unique geographical location, this city has high pollution and high daily temperature variations, so that it is a very appropriate site for study. Altogether, 11 images with very low cloudiness were taken into account, distributed between November 2019 and October 2020. After data validation by means of R2 statistical analysis, the root mean square error (RMSE), mean bias error (MBE) and standard deviation (SD) were determined to obtain the coefficients of correlation. Panel data analysis is presented as a novel element with respect to the methods usually used. Results reveal that the SC algorithms prove more effective and reliable in determining the LST of the city studied here.

Highlights

Where, again, αi are the individual effects, μit is the error of the model, β is coefficient and Before comparing the land surface temperature (LST) obtained by means of the different algorithms, we proceeded to compare the temperature and humidity data obtained from the AEMET meteorological station and the attached probe
The mean differences detected for temperature and relative humidity over the entire period were, respectively, ±0.1 K and ±1%
The algorithms presented higher annual mean values than the ones obtained with dataloggers, except for those reported by authors Wang et al (2015) [28], which were lower but very close values

Summary

Introduction

The land surface temperature (LST) is an essential factor when studying physical processes on earth [1] It is used for reference worldwide, in research dealing with local or regional space and in the fields of hydrology, meteorology, and superficial energy balance [2,3,4], as well as studies surrounding climatic change [5] and recent work involving the phenomenon known as urban heat island (UHI) [6,7,8,9,10]. It can be of vital importance to obtain a reliable LST, and this value could bear an impact upon the quality of life of populations. It has become an essential factor, used to evaluate the interchange of water and superficial energy with the atmosphere [11]

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Results

Discussion

Conclusion