Analysis and precision of the Terrestrial Surface Temperature using Landsat 8 and Sentinel 3 images: Study applied to the city of Granada (Spain)
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2021Subject/s
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Abstract
The use of Landsat 8 and Sentinel 3 satellite images to calculate the land surface temperature (LST) has become one of the most common methods in urban areas. Knowing its precision and seasonal variability will affect future studies of urban climate alteration, such as Urban Heat Island (UHI) that allow sustainable decisions that increase the resilience of cities. In this research, the LST of a medium-sized city, Granada (Spain), was determined using six split window (SW) algorithms: three for Landsat 8 and another three for Sentinel 3A and 3B. Data were validated by measurements in situ at different parts of the city, chosen to represent the different land covers established in the Corine Land Cover inventory (2020). This city has a unique geographical situation, substantial pollution and high daily temperature variations, making it very suitable for study. The data validation by means of statistical analysis gave R2 linear fitting coefficients, the mean square error (RMSE), the mean bias error (MBE) and standard deviation. Our results reveal that the methods based on Landsat 8 SW algorithms present higher mean values (8.16 K) than Sentinel 3 (5.07 K) and Sentinel 3B (2.21 K), the differences being even greater during dry periods. Still, the SWs analyzed can be considered effective and reliable for determining the city's LST. The use of a Convolutional Neural Network (CNN) in improving the quality of satellite images has reported significant improvements in the LST results obtained. © 2021 Elsevier Ltd
The use of Landsat 8 and Sentinel 3 satellite images to calculate the land surface temperature (LST) has become one of the most common methods in urban areas. Knowing its precision and seasonal variability will affect future studies of urban climate alteration, such as Urban Heat Island (UHI) that allow sustainable decisions that increase the resilience of cities. In this research, the LST of a medium-sized city, Granada (Spain), was determined using six split window (SW) algorithms: three for Landsat 8 and another three for Sentinel 3A and 3B. Data were validated by measurements in situ at different parts of the city, chosen to represent the different land covers established in the Corine Land Cover inventory (2020). This city has a unique geographical situation, substantial pollution and high daily temperature variations, making it very suitable for study. The data validation by means of statistical analysis gave R2 linear fitting coefficients, the mean square error (RMSE), the mean bias error (MBE) and standard deviation. Our results reveal that the methods based on Landsat 8 SW algorithms present higher mean values (8.16 K) than Sentinel 3 (5.07 K) and Sentinel 3B (2.21 K), the differences being even greater during dry periods. Still, the SWs analyzed can be considered effective and reliable for determining the city's LST. The use of a Convolutional Neural Network (CNN) in improving the quality of satellite images has reported significant improvements in the LST results obtained. © 2021 Elsevier Ltd
