Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada)
Metadatos
Mostrar el registro completo del ítemEditorial
MDPI
Materia
Landsat 8 images Panel data analysis Land surface temperature Land surface temperature
Fecha
2021Referencia bibliográfica
Hidalgo García, D.; Arco Díaz, J. Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada). Remote Sens. 2021, 13, 1012. https:// doi.org/10.3390/rs13051012
Patrocinador
ERDF (European Rural Development Fund); Ministry of Science and Innovation (State Research Agency) EQC2018-004702-PResumen
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.