Consequence of land–atmosphere interactions, Land surface temperature (LST) is a key parameter for many fields of Geosciences and the determination of the values it assumes in specific areas occurs increasingly through the processing of remote sensing data. The Thermal InfraRed Sensor (TIRS) on board of Landsat-8 and Landsat-9 satellites, for example, provide images acquired in two thermal bands, i.e. B10 (10.60 μm - 11.19 μm) and B11 ( 11.50 μm - 12.51 μm), both with resolution: 100 m x 100 m; these images, through appropriate processing, allow mapping the variability of the LST in the investigated area. Availability of LST values measured in situ at the same time as the thermal image is acquired, would allow to check the accuracy of the satellite derived thermal map. However, data supplied by existing monitoring weather stations refer to near land surface air temperature (NLSAT) rather than LST. This study aims to compare LST values derived from Landsat-9 thermal data with NLSAT to quantify the effects of different land cover types on temperature variations, specifically focusing on data collected on August 12, 2024. Through this comparison, we seek to better understand the spatial distribution of LST and its relationship with urban and rural land cover types, contributing to more effective urban heat mitigation strategies. Corine Land Cover dataset is used to correlate temperature variations between LST and NLSAT at weather station locations across specific land cover types, enabling an analysis of how different soil characteristics influence local temperature patterns. The results confirm that vegetated areas tend to mitigate the temperature differences between the surface and the air above, while artificial areas, especially industrial zones, tend to amplify these differences with higher LST values.
Land surface temperature from Landsat 9 imagery compared with in situ air temperature measurements
Morale D.;Falchi U.;Mercogliano P.;Parente C.
2025-01-01
Abstract
Consequence of land–atmosphere interactions, Land surface temperature (LST) is a key parameter for many fields of Geosciences and the determination of the values it assumes in specific areas occurs increasingly through the processing of remote sensing data. The Thermal InfraRed Sensor (TIRS) on board of Landsat-8 and Landsat-9 satellites, for example, provide images acquired in two thermal bands, i.e. B10 (10.60 μm - 11.19 μm) and B11 ( 11.50 μm - 12.51 μm), both with resolution: 100 m x 100 m; these images, through appropriate processing, allow mapping the variability of the LST in the investigated area. Availability of LST values measured in situ at the same time as the thermal image is acquired, would allow to check the accuracy of the satellite derived thermal map. However, data supplied by existing monitoring weather stations refer to near land surface air temperature (NLSAT) rather than LST. This study aims to compare LST values derived from Landsat-9 thermal data with NLSAT to quantify the effects of different land cover types on temperature variations, specifically focusing on data collected on August 12, 2024. Through this comparison, we seek to better understand the spatial distribution of LST and its relationship with urban and rural land cover types, contributing to more effective urban heat mitigation strategies. Corine Land Cover dataset is used to correlate temperature variations between LST and NLSAT at weather station locations across specific land cover types, enabling an analysis of how different soil characteristics influence local temperature patterns. The results confirm that vegetated areas tend to mitigate the temperature differences between the surface and the air above, while artificial areas, especially industrial zones, tend to amplify these differences with higher LST values.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
