Academic Journal

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

التفاصيل البيبلوغرافية
العنوان: Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada)
المؤلفون: David Hidalgo García, Julián Arco Díaz
المصدر: Remote Sensing; Volume 13; Issue 5; Pages: 1012
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2021
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: Landsat 8 images, panel data analysis, land surface temperature, thermal infrared data
جغرافية الموضوع: agris
الوصف: 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.
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
Relation: Urban Remote Sensing; https://dx.doi.org/10.3390/rs13051012
DOI: 10.3390/rs13051012
الاتاحة: https://doi.org/10.3390/rs13051012
Rights: https://creativecommons.org/licenses/by/4.0/
رقم الانضمام: edsbas.C4AB47A
قاعدة البيانات: BASE