Fusion of Airborne and Spaceborne Thermal Imagery for Temperature Monitoring in Urban Areas

Land use and surface properties directly influence and affect events and phenomena, such as flash flooding due to heavy rains, Urban Heat Island (UHI) intensification during heat waves, biodiversity reduction, etc. To deeply study such phenomena within urban areas, remotely sensed data are essential to describe, investigate, and model them and drive actionable insights. Aerial sensors mounted on airplanes allow for the acquisition of high-resolution data in terms of geometry (up to 5 cm with multispectral cameras) and spectral content (hundreds of narrow bands with wavelengths from visible to short-wave and thermal infrared). In urban applications, images are generally acquired during aerial surveys planned on demand by the municipalities. Satellite images, on the other hand, feature a consistent revisit time thanks to their elliptic sun-synchronous orbits. Satellite images with low spatial granularity, often acquired through international public missions (i.e., Copernicus), are publicly accessible. Within the USAGE—Urban Data Space for Green Deal—EU project [https://www.usage-project.eu/], we investigate the integration of multi-modal and multi-resolution data from aerial and satellite platforms in urban areas for environmental analyses. In this paper, the activities specifically realized in thermal analysis are discussed. Two pilot cities are considered, Graz (Austria) and Ferrara (Italy), where aerial multispectral, thermal, hyperspectral, and LiDAR data, as well as thermal satellite images, are available (Beber et al., Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1/W3-2023, 9–16, 2023). Firstly, the land surface temperatures (LST) are calculated from thermal aerial images (0.5–1 m spatial resolution), also with the support of aerial hyperspectral images (VNIR and SWIR ranges, 1 m spatial resolution) to retrieve information on the type of surface material, thus on the surface emissivity. The LST values are then compared to those retrieved using the Landsat TIRS sensor, in order to characterize the representativeness of the Landsat pixel over the urban landscape. Moreover, a deep learning algorithm to downscale a Landsat product to airborne resolution is presented. Finally, LST maps are coupled with population density to highlight areas with higher risks. The proposed methodology could be replicated also in other similar cities.